Calibration of a wearable medical device

ABSTRACT

A technology for a wearable medical device for monitoring medical parameters. Medical measurement data can be received at the wearable medical device from a medical measurement sensor attached to the wearable medical device or a medical measurement sensor in communication with the wearable medical device. A calibration coefficient can be determined for calibrating the wearable medical device based on the medical measurement data. The wearable medical device can be calibrated based on the calibration coefficient.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/396,705, filed Apr. 28, 2019, which is a continuation of U.S. patentapplication Ser. No. 14/248,334, filed Apr. 9, 2014, the entire contentsof which are incorporated by reference.

BACKGROUND

While providing medical care and treatment, doctors often screen,monitor, and diagnose certain physiological events of their patients.Accordingly, a wide variety of physiological screen, monitor, anddiagnosis devices have been developed to improve patient care. Thephysiological medical devices provide healthcare personnel and patientswith physiological information to more accurately screen, monitor, anddiagnose medical conditions. As a result, physiological medical deviceshave become an indispensable part of modern medicine.

One example of a physiological medical devices used by doctors is apulse oximeter. Pulse oximetry may be used to measure various bloodcharacteristics, such as the arterial blood oxygen saturation ofhemoglobin (SP02) or the rate of blood pulsations corresponding to eachheartbeat of an individual. Continuously monitor a patient'sphysiological condition, such as pneumonia, can require monitoring apatient's heart rate, breathing rate, temperature, and oxygen levels.Continuously monitoring a patient's physiological condition usuallyrequires hospitalization of the patient, which can be both costly andtime consuming, especially where long term monitoring can be required.In addition to often requiring hospitalization, continuously monitor apatient can often require the patient be bed ridden or significantlyreducing a patient's mobility. i.e. a non-ambulatory patient. Recently,wearable medical devices have begun to be developed and used to allow apatient to leave a medical facility while still being monitored.Accurate medical monitoring using wearable medical devices can bedifficult as a patient's physiology and the environment that the patientis monitored in can change over time.

BRIEF DESCRIPTION OF THE DRAWINGS

Features and advantages of the invention will be apparent from thedetailed description which follows, taken in conjunction with theaccompanying drawings, which together illustrate, by way of example,features of the invention; and, wherein:

FIG. 1 depicts a wearable medical device in accordance with an example;

FIG. 2 depicts a wearable medical device and a separate device inaccordance with an example;

FIG. 3 depicts wearable medical device in direct communications with acomputing device in accordance with an example;

FIG. 4 depicts a wearable medical device and a computing device inindirect communication using a communications network in accordance withan example;

FIG. 5 depicts a wearable medical device and a separate device can be inindirect communication using a communications network in accordance withan example;

FIG. 6 depicts a wearable medical device in communication with one ormore other devices in accordance with an example;

FIG. 7 depicts a wristband wearable medical device attached to the wristof an individual in accordance with an example;

FIG. 8 depicts a side view of a wristband wearable medical device inaccordance with an example;

FIG. 9A depicts a wearable medical device at the ankle of an individualin accordance with an example;

FIG. 9B depicts a wearable medical device at the wrist of an individualin accordance with an example;

FIG. 10 depicts a wearable medical device located at the forehead of anindividual in accordance with an example;

FIG. 11 depicts a wearable medical device with sensors in accordancewith an example;

FIG. 12 depicts a wearable medical device in accordance with an example;

FIG. 13 depicts a wearable medical device with a plurality of medicalmeasurement sensor or physiological measurement sensor in accordancewith an example;

FIG. 14 depicts a wearable medical device that can be in communicationwith separate device in accordance with an example;

FIG. 15 depicts a wearable medical device forecasting the future ofselected physiological data in accordance with an example;

FIG. 16A depicts a wearable medical device that can receive selectedcalibration information in accordance with an example;

FIG. 16B depicts a wearable medical device that can receive data orinformation from an external device in accordance with an example;

FIG. 17 depicts illustrates selected locations where a wearable medicaldevice can be attached to the body of an individual in accordance withan example;

FIG. 18 depicts the functionality of the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 19 depicts the functionality of another computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 20 depicts the functionality of another computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 21 depicts the functionality of another computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 22 depicts the functionality of another computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 23 depicts the functionality of another computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 24 depicts the functionality of another computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 25 depicts the functionality of another computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 26 depicts the functionality of another computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 27 depicts the functionality of another computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 28 depicts the functionality of another computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 29 depicts the functionality of another computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 30 depicts the functionality of another computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 31 depicts the functionality of another computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 32 depicts the functionality of another computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 33 depicts the functionality of another computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 34 depicts the functionality of another computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 35 depicts the functionality of another computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 36 depicts the functionality of another computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 37 depicts the functionality of another computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 38 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 39 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 40 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 41 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 42 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 43 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 44 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 45 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 46 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 47 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 48 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 49 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 50 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 51 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 52 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 53 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 54 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 55 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 56 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 57 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 58 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 59 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 60 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 61 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 62 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 63 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 64 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 65 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 66 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 67 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 68 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 69 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 70 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 71 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 72 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 73 depicts the functionality of another the computer circuitry of awearable medical device that is operable calibrate sensors of thewearable medical device or medical measurements of the wearable medicaldevice in accordance with an example;

FIG. 74 illustrates a physiological measurement device 7410 for taking aphysiological measurement of a user in accordance with an example; and

FIG. 75 illustrates a method for method for calibrating a wearablemedical device, in accordance with an example.

Reference will now be made to the exemplary embodiments illustrated, andspecific language will be used herein to describe the same. It willnevertheless be understood that no limitation of the scope of theinvention is thereby intended.

DETAILED DESCRIPTION

Chronic medical conditions are a major health concern worldwide. Chronicinfirmities and diseases consume the majority of healthcare expendituresas treatment for chronic infirmities and diseases can be for anopen-ended or indefinite duration of time. For example, congestive heartfailure (CHF) is a progressive disease with varying symptoms such asfatigue, shortness of breath, fluid retention, swelling in the abdomenor legs, lung congestion, and cardiac arrhythmias. CHF can be treated,and its symptoms mitigated, by lifestyle modifications and constantmonitoring of a patient. However, monitoring CHF can be time intensiveand difficult. Accordingly. CHF continues to reduce the quality of lifeand life expectancy of CHF patients.

Many chronic medical conditions are traditionally monitored on aperiodic or sporadic basis. i.e. not monitored on a continuous orsemi-continuous basis. For example, blood pressure measurements, bodytemperature measurements, hydration, and other measurements are onlymeasured and collected at prescribed intervals.

Wearable medical devices can be used to enable constant, continuous,semi-continuous, or periodic monitoring of medical conditions, such asCHF, and decrease the difficulty of a patient or medical personnel, suchas a caregiver, in monitoring a medical condition. Wearable medicaldevices can enable patient monitoring that can be constant,non-invasive, transparent, and/or non-intrusive into the patient's life.For example, wrist-worn devices have been developed to record apatient's physiological data, such as a patient's heart rate, level ofactivity, etc.

Medical patients can often be mobile, i.e. moving around, while wearinga medical device. Additionally, a patient or user can move, shift, orremove a wearable medical device for certain occasions, such asshowering, or during normal use of the medical device. Traditionally,movement of a wearable medical device and taking the wearable medicaldevice on and off can reduce or limit the accuracy of physiologicalinformation from wearable medical devices. Accurate medical monitoringcan also be difficult where a patient's physiology can change over time,the environment that the wearable medical device can be used in maychange or vary, the wearable medical device can lose calibration, and soforth.

As the environment that the wearable medical device can be used inchanges and as variations occur in a patient's physiology, providingaccurate readings for a desired physiological condition or monitorselected medical measurements over a selected period of time, such ashours, days, or weeks can become increasingly difficult. In oneembodiment, the wearable medical device can measure selectedphysiological measurements while filtering out other physiologicalmeasurements that can interfere with the selected physiologicalmeasurements. For example, the wearable medical device can measure aheart rate, a hydration level, and a blood oxygen level of an individualwhile filtering out physiological effects due to medication, dietarychanges, and so forth. In another embodiment, movement artifacts,environmental interference, and so forth, can adversely affect thecalibration and reliability of the data generated by the wearablemedical device when not properly accounted for and/or calibrated for.

FIG. 1 shows one exemplary embodiment of the wearable medical device.FIG. 1 illustrates that the wearable medical device 110 can be awearable band, such as a wristband, headband, armband, chest band, legband, or band attached to an individual at a selected location. Thewearable medical device 110 can include a display 120 to showinformation to a user or a third party. The wearable medical device 110can also include one or more integrated or attached sensors 130, asdiscussed in the proceeding paragraphs.

The sensors or measurement data of the wearable medical device can beadjusted, calibrated, and recalibrated to increase the accuracy andreliability of wearable medical device. For example, physiologicalmeasurements and medical measurements taken by sensors attached to orintegrated into the wearable medical device can be adjusted, calibrated,and recalibrated. Additionally, the medical measurements taken by thewearable medical device can be analyzed, filtered, adjusted, and soforth, to increase the accuracy and reliability of the physiologicalmeasurements and medical measurements of the wearable medical device.There are several techniques for calibrating wearable medical devicesand analyzing the medical measurement data. In one embodiment, thecalibration techniques discussed in the proceeding paragraphs can beapplied to calibrating one or more sensors of the wearable medicaldevice or separate device and/or measurement data taken by the one ormore sensors of the wearable medical device or separate device. Inanother embodiment, the calibration techniques discussed in theproceeding paragraphs can be applied before one or more measurements aretaken by the one or more sensors (e.g. apriori), real time (e.g. whilethe one or more sensors are taking one or more measurements), or afterthe measurement are taken by the one or more sensors (e.g. postmeasurement activity calibration).

One technique for calibrating the wearable medical device can be to usea device separate or independent for the wearable medical device to takephysiological measurements. FIG. 2 shows a wearable medical device 210and a separate device 220. FIG. 2 further illustrates that the wearablemedical device 210 can be in communication with the separate device 220,using a communication network 230 In another embodiment, thecommunications network can be a cellular network that may be a 3GPP LTERel. 8, 9, 10, 11, or 12 or IEEE 802.16p. 802.16n, 802.16m-2011,802.16h-2010, 802.16j-2009, 802.16-2009. In another embodiment,communications network can be a wireless network (such as a wirelessfidelity network (Wi-Fi) that may follow a standard such as theInstitute of Electronics and Electrical Engineers (IEEE) 802.11-2012,IEEE 802.11ac, or IEEE 802.11ad standard. In another embodiment, thecommunications network can be a Bluetooth connection such as Bluetoothv1.0, Bluetooth v2.0, Bluetooth v3.0, or Bluetooth v4.0. In anotherembodiment, the communications network can be a Zigbee connection suchas IEEE 802.15.4-2003 (Zigbee 2003), IEEE 802.15.4-2006 (Zigbee 2006),IEEE 802.15.4-2007 (Zigbee Pro). In one embodiment, the separate device220 can be another wearable medical device separate from the wearablemedical device 210. In another embodiment, the separate device 220 canbe a device unattached to the user.

The physiological measurements taken by the separate device 220 can beused to set a baseline or basis for comparison for the physiologicalmeasurements of the wearable medical device 210. The baselinephysiological measurements taken by the separate device 220 can be usedto calibrated and/or recalibrate the wearable medical device 210. In oneembodiment, the user can take a baseline measurement for a selectedphysiological measurement of the user using the separate device 220. Inanother embodiment, the separate device 220 can automatically take abaseline measurement for a selected physiological measurement of theuser. The separate device 220 can communicate the baseline physiologicalmeasurement to the wearable medical device 210 and the wearable medicaldevice 210 can use the baseline physiological measurement as a basis forcalibrating the wearable medical device 210. In one embodiment, thewearable medical device 210 can use the baseline physiologicalmeasurement as a basis for calibrating the wearable medical device 210for the selected physiological measurement. Physiological measurementscan include, measurements of characteristic of the health of anindividual, measurements of bodily functions of an individual, chemicalbalances of an individual, psychological functions of an individual,medical measurements, and so forth.

In one embodiment, the separate device 220 can be used to uniquelycalibrate the wearable medical device 210 to each unique user of thewearable medical device 210. For example, the wearable medical device210 can monitor the hydration status or change in the hydration level ofthe unique user. As each unique user of the wearable medical device 210has a different physiology, measuring the baseline physiologicalmeasurement from which to measure the user's hydration level isdifferent for each unique user. The separate device 220 can be used tocharacterize a user signature, e.g. a unique identifier of the user, andcalibrate one or more sensors 240 of the wearable medical device foreach unique user based on the baseline measurements of the separatedevice 220. In one embodiment, the separate device 220 can be aurinalysis device, a spectrometer, a pulse oximeter, a heart ratemonitor, a body weight measurer, a body mass measurer, a blood pressuremonitor, a pedometer, an implantable device, an electrocardiogram (EKG)device, and so forth.

For example, the physiology of a teenage girl can be different from thephysiology of an elderly man. These physiological differences caninclude general health, weight, percent body fat, metabolic rates, age,sex, general health, average level of activity, race, geneticpredispositions, and so forth. Accordingly, to more accurately monitorthe hydration level of the teenage girl or the elderly man, the wearablemedical device can be calibrated to each individual. In one embodiment,a baseline calibration from which relative physiological measurementscan be made can be set or established for each user, such as thehydration level of the individual. For example, a body mass measuringdevice can measure the body mass of the individual and the body mass canbe used to calibrate the wearable medical device for monitoring thehydration level of the individual wearing the wearable medical device.In one embodiment, the body mass measurer can be a body weight scale ora bathroom scale that measures the weight of the individual. The heightof the individual can be measured or input and the body mass measurercan convert the weight and height of the individual into a body massindex. In one embodiment, the body mass of an individual can bemonitored over a selected period of time, such as 3 or 4 days. When thebody mass of the individual remains approximately constant over theselected period of time, then the body mass measurement can be used toset a base line for calibrating the wearable medical device.

FIG. 3 illustrates that the wearable medical device 310 can be in directcommunications 330 with an other computing device 320. In oneembodiment, the direct communication 330 can be a Bluetooth. Zigbee,radio signal, or other direct communication systems. In one embodiment,the other computing device 320 can be a server that stores informationsuch as previously physiological measurements taken by the wearablemedical device 310 or physiological measurements taken from a selectedgroup of individuals, as discussed in the proceeding paragraphs. Inanother embodiment, the other computing device 320 can be a mobilecomputer device, such as a laptop computer, tablet, or a smartphone. Thewearable medical device 310 can communicate data, such as physiologicalmeasurements, to the other computing device 320. In one example, theother computing device 320 can be used to process and/or analyze thedata communicated from the wearable medical device 310. In anotherexample, the computing device 320 can communicate the processed data,analyzed data, measurement results, or other information to the wearablemedical device 310. In another example, the computing device 320 cancommunicate calibration information to the wearable medical device 310.

FIG. 4 illustrates that a wearable medical device 410 and a computingdevice 420 can be in indirect communication using a communicationsnetwork such as wireless communication network 430, such as a Wi-Finetwork, and/or using a cellular communication network 440, such as a3GPP network, to communicate data or information. In one embodiment, thewearable medical device 410 can take physiological or medicalmeasurements using one or more sensors 450 and communicate thephysiological or medical measurement data to the computing device 420via the wireless communication network 430 and/or the cellularcommunication network 440. In another example, the computing device 420can receive physiological or medical measurement data from the wearablemedical device 410 via the wireless communication network 430 and/or thecellular communication network 440 and process the data and/or analyzethe data. When the computing device 420 has analyzed process the dataand/or analyze the data, the computing device can communicate theprocessed data, analyzed data, measurement results, or other informationto the wearable medical device 410 via the wireless communicationnetwork 430 and/or the cellular communication network 440.

FIG. 5 illustrates that a wearable medical device 510 and a separatedevice 520 can be in indirect communication using a communicationsnetwork such as wireless communication network 530, such as a Wi-Finetwork, and/or using a cellular communication network 540, such as a3GPP network, to communicate data or information. In one embodiment, thewearable medical device 510 and/or the separate device 520 can takephysiological or medical measurements. In one example, the wearablemedical device 510 can communicate the physiological or medicalmeasurements to the separate device 520 using the communication network.In one example, the separate device 520 can communicate thephysiological or medical measurements to the wearable medical device 510using the communication network. In one embodiment, the wearable medicaldevice 510 and/or the separate device 520 can use the receivedphysiological or medical measurements to calibrate one or more sensorsor measurement data of the wearable medical device 510 or the separatedevice 520, respectively.

FIG. 6 illustrates that the wearable medical device 610 can be incommunication with one or more other devices, such as a computing device620, a weight scale 630, an electrocardiography (ECG) device 640, and/ora server 650. In one embodiment, the wearable medical device 610 canreceive medical or physiological data, user information, and/ormeasurement information from the one or more other devices 620-650. Inone embodiment, the wearable medical device 610 can uses the medical orphysiological data, user information, and/or measurement information tocalibrate the wearable medical device 610, one or more sensors of thewearable medical device 660, or measurement data of the one or moresensors of the wearable medical device 660. In another embodiment, thewearable medical device 610 can communicate medical or physiologicaldata, user information, and/or measurement information to the one ormore other devices 620-650.

FIG. 7 shows one exemplary embodiment of a wristband wearable medicaldevice 710. The wristband wearable medical device 710 can attach to thewrist of an individual and take one or more medical measurements at thewrist location. In one embodiment, wristband wearable medical device 710can cover or wrap around the circumference of the wrist of anindividual. FIG. 8 shows a side view of a wristband wearable medicaldevice 810, as discussed in FIG. 7. The wristband wearable medicaldevice 810 can have one or more integrated sensors 820. The wristbandwearable medical device 810 can have a flat top portion 830 and acircular remaining portion 840 to fit to the contour or shape of a wriston an individual.

FIGS. 9a and 9b illustrate a wearable medical device 910 located atselected locations on an individual. FIG. 9a illustrates a wearablemedical device 910 located at or near the ankle of an individual. FIG.9b illustrates a wearable medical device 910 located at or near wrist ofan individual. FIG. 10 illustrates a wearable medical device 1010located at a selected location on an individual. FIG. 10 illustrates awearable medical device 1010 located at or near head or forehead of anindividual.

The separate device can be used to recalibrate or check the accuracy ofthe wearable medical device. For example, the separate device can takecontinuous or periodic physiological measurements over a selected timeperiod. The physiological measurements taken by the separate device canbe compared to measurements taken by the wearable medical device. Forexample, the body mass measure can monitor the body mass of the userover a defined period of time to determine a consistent body mass of theindividual over time or changes in the body mass of the individual oftime. The body mass measurements can be correlated with the measurementstaken from the individual using the wearable medical device. In oneembodiment, the body mass measurements of the individual can signal aphysiological change and the wearable medical device can be recalibratedto correlate with the body mass measurements.

In one embodiment, when changes in the physiological measurements aredetected in the data taken by the independent medical device, such aschanges in the body mass of the individual, the physiologicalmeasurement changes can be compared to measurements taken by thewearable medical device. When changes in physiological measurementstaken by the independent medical device correlate with the wearablemedical device, the wearable medical device can determine that thewearable medical device is taking accurate or correct measurements. Inanother embodiment, when changes in physiological measurements taken bythe separate device do not correlate or are outside a selectedcorrelation range with the measurements taken by the wearable medicaldevice, the wearable medical device can determine that the measurementtaken by the wearable medical device are inaccurate or incorrectmeasurements and the wearable medical device can be recalibrated. In oneembodiment, the correlations or cross correlations can provideadditional measurement information or calibration information. In oneembodiment, the correlations or cross correlations can be used toincrease the accuracy of a physiological measurement. For example, thephysiological measurement taken by the independent medical device canprovide different or separate data points that correlate with other datapoints take by the wearable medical device. When the data points arecorrelated between the two devices, the accuracy of the measurement datacan be increased because of a larger or more detailed correlated dataset.

For example, a sudden increase in the body mass or weight of anindividual may signal the retention of water and other bodily fluids.There are several techniques for monitoring an increase or retention ofbodily fluid. In one embodiment, an increase or retention of bodilyfluid can be monitored by observing a weight lose and/or gain of anindividual for a defined or selected period of time. For example, theweight lose and/or gain of the individual can be measured by weighingthe individual multiple times for a select period of time, such as theindividual weighing himself on a bathroom scale at selected points intime.

In another embodiment, pressure plates can be placed under the legs of abed or mattress to monitor changes in weight or body mass of the user ofthe wearable medical device. For example, assuming factors other thanthe weight of the individual are held constant or filtered out, then thedifference between measurements of the empty weight of the bed andmeasurements of the weight bed with the individual can be monitored tomeasure the change in the weight or body mass of the user of thewearable medical device. One advantage of using pressure plates to weighan individual, such as ones placed under the legs of a bed, can be toenable automatic and nonintrusive monitoring of the individual's weightor body mass. Automatic and nonintrusive monitoring of the individual'sweight or body mass can eliminate the need for the individual toremember to weigh himself in order to calibrate a wearable medicaldevice. Additionally, using pressure plates to weigh an individual canenable measuring the weight of the individual at approximately the sametime each day to reduce the influence of external factors such as weightchange before or after the intake of food that can create falsepositives.

When the wearable medical device does not show a corresponding change inphysiological measurement data of the individual, such as the hydrationlevel of an individual, the wearable medical device can be recalibratedto more accurately reflect the change in the body mass of theindividual.

In one embodiment, the body mass of an individual can be measured usingurine analysis, skin tension, hematocrit (HCT), and so forth.Physiological measurements, such as body mass, can be correlated withother physiological events of the body of an individual, such as waterretention, and used to calibrate the wearable medical device for one ormore physiological measurements. In one embodiment, a plurality ofphysiological measurements taken using one or more separate devicesand/or sensors of the wearable medical device can be analyzed usingmultivariant analysis of the plurality of physiological measures tomaintain calibration and accuracy of the wearable medical device.

The wearable medical device can monitor the hydration level of anindividual using impedance spectroscopy. For example, the wearablemedical device can monitor trending of the impedance of an individual'sskin or subdermal body location with impedance spectroscopy. Oneadvantage of using non-invasive monitor, such as impedance spectroscopy,in a wearable medical device can be to enable non-invasive monitor of anindividual over an extended period of time. Another advantage of usingnon-invasive monitor, such as impedance spectroscopy, in a wearablemedical device can be to enable automatic and nonintrusive taking ofphysiological measurement from the individual.

In another embodiment, the wearable medical device and/or the separatedevice can take non-invasive, automatic, continuous, and/ornon-intrusive physiological measurements. One advantage of takingnon-invasive, automatic, continuous, and/or non-intrusive physiologicalmeasurements can be that the user of the wearable medical device and/orthe separate device can be unaware or oblivious to the wearable medicaldevice and/or the separate device taking the physiological measurements.For example, the individual can be unaware or oblivious to the wearablemedical device and/or the separate device taking the physiologicalmeasurements until the wearable medical device alerts the user whenselected or defined physiological event occurs.

For example, the wearable medical device can use impedance spectroscopyto take hydration level measurements and monitor the hydration level ofthe user, receive body mass measurement of the user from a separatedevice, and recalibrate the wearable medical device based on thehydration level measurements and body mass measurements. Anotheradvantage of non-intrusive and/or automatic medical measurements can befor long term monitoring and/or treatment of patients, where transparentand seamless patient engagement with the wearable medical device canenable the long term care patient to live a life without a constantinterruption of inputting information into a device, e.g. the long termcare patient can live normally during the treatment period.

In one embodiment, the wearable medical device can continuously monitorthe user and alert the user of a medical or physiological condition whenit occurs. One advantage of continuously monitoring the user using thewearable medical device can be that the user can receive alerts earlierthan a medical device that periodically takes medical measurements, suchas an independent or separate device. For patients that require closemedical monitoring, such as CHF patients, continuous monitoring andearly detection can enable the patient to avoid more serious medicalconditions.

The wearable medical device taking continuously or semi-continuouslyphysiological measurements can enable a more detailed trend analysis ofthe physiological measurements. In one embodiment, trend analysis ofphysiological measurements can provide different information than asingle or distinct physiological measurement. For example, monitoringweight gain or loss trend of an individual can show a sudden orunexpected change in the body weight of the individual. The sudden orunexpected change in the body weight can indicate physiological changes,whereas an absolute or distinct body weight measurement of theindividual may not show the sudden or unexpected change in weight. Oneadvantage of using the wearable medical device to monitor trendinginformation can be to collect physiological or medical informationand/or measurements that a device that takes absolute or distinctphysiological or medical information and/or measurements may notcapture.

The measurements taken by the separate device can be aggregated orcombined with the measurements taken by the wearable medical device. Inone embodiment, the measurements taken by the separate device can beuncorrelated, unrelated, or orthogonal to the measurements taken by thewearable medical device. The unrelated measurements taken by theseparate device can be aggregated with the measurements taken by thewearable medical device to provide additional detail of the user'sphysiological condition. For example, the oxygen level of an individualcan be measured by using a separate device and the oxygen level can beunrelated to a hydration level measurement taken by the wearable medicaldevice. In one embodiment, the distinct and unrelated oxygen levelmeasurement and hydration level measurement can be correlated toincrease the accuracy of the physiological data provided to the user,patient, or caregiver.

In another embodiment, the unrelated measurements taken by the separatedevice and the wearable medical device can be associated to the samephysiologic or medical condition, such as the hydration level of apatient. The measurement taken by the separate device and themeasurement taken by the wearable medical device for the samephysiologic condition can then be aggregated or correlated to provideadditional detail and accuracy regarding the physiologic condition ofthe user. For example, the separate device can measure the oxygen levelof the user and the wearable medical device can measure the bioimpedanceof the user. In this example, while the oxygen level of the user may notaffect the bioimpedance of the user, both measurements may be associatedwith the hydration level of the user and each measurement can beaggregated together to provide a more accurate and detailed data setregarding the user's hydration state. One advantage of aggregating themeasurements of the wearable medical device with the measurements of theseparate device can be to increase the accuracy of the informationand/or calibrate the measurements taken by the separate device and/orthe wearable medical device. In another embodiment, multiplemeasurements, such as measurements taken by the wearable medical deviceand measurements taken by the separate device, can be analyzed inaggregation to enable additional or differential diagnosis, monitoring,and screen of medical and/or physiological conditions of the user. Forexample, monitoring a patient's hydration level or state in conjunctionwith the patient's absolute weight or weight changes can enable acaregiver to differentiate when a weight gain can be caused by waterretention or caused by some other issue.

Another technique in calibrating the wearable medical device can be toset or define a threshold value that when exceeded can trigger thecalibration of the wearable medical device. In one embodiment, thethreshold value can be for a measurement taken by the separate orindependent device. In another embodiment, the separate device can be aspectrometer or a specific gravity device used to measure targetparameters of the individual, such as specific substances, cells, orproperties. In one embodiment, the target parameters can be urine color,specific gravity of the urine, saline content, and so forth. In oneembodiment, when a target parameter exceeds a defined threshold thewearable medical device can be calibrated.

In one embodiment, threshold values can be set on the maximum plausibleor probable change of a selected physiological parameter within adefined or selected period of time. For example, a rate that cells (i.e.as red blood cells), nerves, tissue, and so forth of an individual'sbody can regenerate can have specific maximum values. Physiologicalindicators such as an individual's oxygen saturation level, pulse rate,blood pressure, and so forth can each have specific maximum values. Inone embodiment, the distribution of data point collected by the wearablemedical device and/or the separate device can be analyzed to determinewhen a data point or set of data points exceeds or is outside a selectedor desired data set distribution.

A selected data point collected by the wearable medical device and/orthe separate device can be compared with a continuous or ongoingcollection of data points or data sets collected by the wearable medicaldevice and/or the separate device and analyzed to determine when theselected data point exceeds or falls outside a selected threshold orstandard deviation, such as 3 standard deviations. In anotherembodiment, selected data points or data sets can be analyzed todetermine when one or more discontinuities in the selected data pointsor data sets occur or take place. In another embodiment, selected datapoints or data sets can be analyzed to determine when sudden and/orunexpected reversals or turnarounds in the selected data points or datasets occur or take place.

One or more target parameters for a medical measurement can be comparedwith data taken by the wearable medical device for the same medicalmeasurement to determine an error rate of the medical measurement takenby the wearable medical device. For example, if a specific gravitydevice is used on an individual's urine sample to determine that theindividual is dehydrated to a defined degree, the same hydrationmeasurement can be taken for the individual using the wearable medicaldevice. In another embodiment, a hydration measurement can be taken bymeasuring: isotope dilution; bioelectrical impedance; plasma markers,such as osmolality, sodium, total protein, hematocrit, or hemoglobin;hormone concentrations; urine color; changes in body mass; salivary flowor gross, or other physical signs and symptoms of clinical dehydrationfor the individual.

The difference between the results from a specific gravity device andthe wearable medical device can be used by the wearable medical deviceto determine the error rate or level of the measurements taken by thewearable medical device. In one embodiment, when the error rate of thewearable medical device exceeds a selected or defined threshold, thewearable medical device can be calibrated. In another embodiment, thethreshold value can be for a measurement taken by the wearable medicaldevice. When a medical measurement taken by the wearable medical deviceexceeds a selected threshold value, the wearable medical device can becalibrated. In another embodiment, when the medical measurement taken bythe wearable medical device exceeds a selected threshold value, an errorcan be indicated to the user and/or a third party signaling that thewearable medical device may not be functioning properly and/or thewearable medical device should be checked to verify the wearable medicaldevice functioning properly.

Another technique for calibrating the wearable medical device can be touse a selected or defined sensor integrated into the wearable medicaldevice or internal to the wearable medical device, e.g. a sensor builtinto the wearable medical device. The integrated or internal sensor canbe used to set a baseline from which to calibrate and/or recalibrate thewearable medical device and/or other selected sensors of the wearablemedical device.

In one embodiment, the internal sensor can be a plethysmograph. Aplethysmograph can be an instrument for measuring changes in volumewithin an organ. The plethysmograph can attach to an arm, leg or otherextremity of the individual and be used to determine circulatorycapacity. The wearable medical device can attach around thecircumference of a body part of the individual, such as a wristbandaround the wrist of an individual. In one embodiment, the internalsensor can be an air plethysmograph, wherein the air plethysmograph canbe an air-filled cuff used to measure the change in circumference of theorgan of an individual. The air plethysmograph can measure and/ormonitor a complete circumference or partial circumference of the bodypart of the individual where the air plethysmograph is located.

A change in the circumference can indicate a change in a physiologicalstate of the individual. For example, when the circumference of the bodypart of the individual increases, the increase in circumference canindicate an increase in the fluid level or swelling of an organ of theindividual and/or an increase in the fluid level or swelling of theindividual overall. When the circumference of the individual's body partdecreases, the decrease may indicate a decrease in the fluid level of anorgan of the individual and/or an decrease in the fluid level of theindividual overall. Sudden increases and decreases in thecircumferential measurements can be seen in the circumferencemeasurements of dialysis patients.

In another embodiment, the internal sensor can be a non-invasiveimpedance plethysmograph (IPG). The non-invasive IPG can detect venousthrombosis for a selected area of the body of an individual. The IPG canmeasure small changes in electrical resistance of selected areas of thebody, such as the chest, calf, or wrist. In one example, the electricalresistance measurements can reflect blood volume changes and canindicate the presence or absence of venous thrombosis.

In another embodiment, a hematocrit of an individual can be taken usingan internal sensor of the wearable medical device and can be used tocalibrate the wearable medical device or a selected sensor of thewearable medical device. The hematocrit can be a volume percentage ofred blood cells in the blood of the individual. In one embodiment, anultrasonic sensor can measure the hematocrit of the individual bymonitoring changes in an ultrasound wave velocity propagation in plasmaas a function of the red blood cell concentration. In anotherembodiment, a Doppler ultrasound measurement can be used to measure thehematocrit of the individual. In another embodiment, an interferometercan be used to measure the hematocrit of the individual. In anotherembodiment, a spectrometer can be used to measure the hematocrit of theindividual.

In one embodiment, the wearable medical monitoring device can usemeasurements from the internal sensor of the wearable medical device tocalibrate the wearable medical device or selected sensors of thewearable medical device. In another embodiment, the wearable medicaldevice can aggregate a measurement from the internal sensor with othermeasurements taken by the wearable medical device to provide additionaldetail and accuracy for medical measurement information.

Another technique to calibrate the wearable medical device can be to usemeasurement information from a plurality of sensors, such as a sensorarray. The sensor array can include an oxygen saturation sensor, atemperature sensor, an accelerometer, a gyroscope, a plethysmographsensor, an internal calibration sensor, and so forth. In one embodiment,the wearable medical monitoring device can collect measurementinformation from the plurality of sensors in the sensor array andperform a multivariate analysis (MVA) to calibrate the wearable device.MVA can be used to analyze more than one variable at a time and can beperformed across multiple dimensions while taking into account theeffects of more than one variable of interest. In another embodiment, aMonte Carlo simulation can be used to analyze measurements from theplurality of sensors or sensor array and calibrate the wearable medicaldevice.

MVA can be performed on measurements taken by one or more sensors of thewearable medical device or sensor array and one or more sensors of aseparate device. In one embodiment, the MVA can be performedsequentially on the measurements taken by the wearable medical deviceand the measurements taken by the separate device. In anotherembodiment, MVA can be simultaneously performed on the measurementstaken by the wearable medical device and the measurements taken by theseparate device.

A regression analysis, such as a partial least square (PLS) analysis,can be used to calibrate the wearable medical device. The regressionanalysis can predict a continuous dependent variable from a plurality ofindependent variables. In one embodiment, the dependent variable can bea dichotomous and a logistic regression can be used to calibrate thewearable medical device. The plurality of independent variables used inthe regression can be either continuous or dichotomous. In anotherembodiment, linear regression can be used to calibrate the wearablemedical device. In another embodiment, non-linear regression can be usedto calibrate the wearable medical device.

The wearable medical device can filter the measurement data orinformation collected from one or more of the sensors, such as sensorsin the sensor array, of the wearable medical device. For example, aspectral sensor can be used to collect data or information. The spectralsensor can emit multiple wavelengths or frequencies of light and thewearable medical device can filter out undesirable or selectedfrequencies or wavelengths so that only desirable or selectedfrequencies or wavelength remain to be analyzed. In one embodiment, anoutlying or erroneous measurement, such as an extreme value or a valuethat significantly deviates from a data set, can be filtered out orexcluded from the measurement information used to calibrate the wearablemedical device. When measurement data from a sensor of the wearablemedical device or a separate device exceeds a defined threshold valuefor the measurement data, the measurement data can be excluded from dataused to calibrate the wearable medical device.

In another embodiment, when the measurement from the sensor of thewearable medical device or the separate device exceeds a definedthreshold value for the measurement data, the measurement data from thesensor of the wearable medical device or the separate device can beexcluded from the measurement data used to determine physiologicalmeasurements. In another embodiment, when the measurement from thesensor of the wearable medical device or the separate device exceeds adefined range for the measurement data, the measurement data from thesensor of the wearable medical device or the separate device can beexcluded from data used to calibrate the wearable medical device andfrom the measurement data used to determine physiological measurements.

Another calibration technique can be to use a learning algorithm orsmart algorithm to calibrate the wearable medical device. In oneembodiment, the learning algorithm or smart algorithm can use data froma plurality of individuals to calibrate the wearable medical device. Forexample, measurement data can be collected from a plurality ofindividuals (group data), where the plurality of individuals can havesimilar selected criteria or characteristics such as age, weight,fitness level, gender, ethnicity, and so forth. The group data can beanalyzed to determine a calibration coefficient to calibrate thewearable medical device for the individual using the wearable medical.

A histogram based on the group data for the user of the wearable medicaldevice can be used to determine the appropriate calibration coefficient.For example, an individual can input defined user information, such asthe age, weight, fitness level, and gender of the user and the wearablemedical device can select the appropriate group data based on theinputted user information to calibrate the wearable medical device. Inone embodiment, the wearable medical device can recursively oriteratively analyzed the data until the data reaches a stable state. Inone embodiment, a stable state for the data can be when the accuracyrange or error rate reaches a selected threshold or threshold range. Forexample, the wearable medical device can iteratively calibrate andrecalibrate a sensor of the wearable medical device and/or the data froma sensor of the wearable medical device until the sensor providesmeasurements within a selected threshold range and/or the data from asensor is within a selected threshold range. In another embodiment, thewearable medical device can iteratively calibrate and recalibrate asensor of the wearable medical device and/or the data from a sensor ofthe wearable medical device for a selected period of time and/or for aselected number of cycles. In one embodiment, the wearable medicaldevice can use recursively or iteratively, such as a smart algorithm orlearning algorithm, to analyze a physiological or medical measurement ordata set until reaching a stable state. In one embodiment, recursion canbe a process whereby a solution of a calculation determined through analgorithm, such as a solution determined by the wearable medical deviceusing the smart algorithm or learning algorithm, is fed back into thealgorithm and recalculated, wherein the recursive analysis can repeateduntil the algorithm reaches a stable state. In one embodiment, a stablestate can be based on an optimal or efficient solution for thecalibration of the wearable medical device.

Another technique for calibrating the wearable medical device can be toaccount for or compensate for the movement of the wearable medicaldevice. In one embodiment, when the wearable medical device determinesthere has been a sudden movement of the wearable medical device ordiscontinuity of measurement data, the wearable medical device mayrecalibrate the wearable medical device. In another embodiment, when thewearable medical device determines there has been a sudden movement ofthe wearable medical device, the wearable medical device can set a newbaseline that one or more sensors of the wearable medical device measurefrom. In another embodiment, when the wearable medical device determinesthere has been a sudden movement of the wearable medical device, thewearable medical device can set a new baseline that the wearable medicalcan use to analyze measurement data from one or more sensors of thewearable medical device. A sudden movement of the wearable medicaldevice can be indicated in the data by a discontinuity, break, or gap ina continuous data set or semi-continuous data set taken using one ormore sensors of the wearable medical device. In one embodiment, thebaseline value is readjusted to remove a discontinuity, such as adiscontinuity in measurement data, a discontinuity in sensormeasurements, a discontinuity in sensor location, and so forth.

In one embodiment, the wearable medical device can detect a suddenmovement of the wearable medical device and indicate to the user thatthe wearable medical device needs to be relocated back to the definedlocation in order to continue monitoring the individual. In anotherembodiment, the wearable medical device can determine a defined orselected value to offset a data set from one or more of the sensors ofthe wearable medical device based on a new or different location of thewearable medical device on the individual, such as a new location of thewearable medical device cause by the movement of the wearable medicaldevice.

FIG. 11 illustrates a wearable medical device 1110 with one or moresensors 1120, such as a accelerometer. 3d accelerometer, or gyroscope,to determine if there has been an abrupt or sudden acceleration ordeceleration, and/or a sudden or abrupt movement of the wearable medicaldevice that may have caused the wearable medical device to movelocations on the user. The one or more sensor 1120 can detect movementof the wearable medical device 1110 in the x-axis direction, y-axisdirection, and/or the z-axis direction. In one embodiment, a sudden orabrupt movement of the wearable medical device 1110 detected by the oneor more sensors 1120 can be a shift, gap, or jump in movement data. Forexample if one of the sensors 1120 previously measured a zero orsubstantially minimal movement in the x-axis direction and then measuresa substantial increase in movement in the x-axis direction, thesubstantial increase of movement in the x-axis direction can indicate amove of locations of the wearable medical device 1110 on the user.

In another embodiment, the wearable medical device can include a lightsensor or optical sensor to determine if the wearable medical device hasmoved locations on the user. For example, the optical sensor of thewearable medical device can shine a light onto the skin of the user andthe wearable medical device can have a light receiver to detectreflected light from the skin of the user. When the light receiverdetects a sudden change in the reflected light from the skin of theuser, the sudden change in the reflected light can indicate that thewearable medical device has moved or shifted. In another embodiment, thewearable medical device can include one or more sensors to monitorexternal environmental conditions and/or changes in externalenvironmental conditions, such as temperature, humidity, light, and soforth. The wearable medical device can determine the effects of theexternal environmental conditions and/or changes in externalenvironmental conditions and adjust or correct shifts in measurements byone or more sensors of the wearable medical device.

The wearable medical device can use historical trending data orinformation from one or more sensors of the wearable medical deviceand/or a separate device to calibrate the wearable medical device. Inone embodiment, the wearable medical device can record and/or storeprevious measurement data of the individual taken by one or more sensorsof the wearable medical device and/or the separate device. The wearablemedical device can analyze the historical data to determine when thecurrent measurement data is likely correct or probable for the user. Inone embodiment, the current measurement data can be likely correct orprobable for the user when the current measurement data is in a selectedrange. The selected range can be a range of the historical data, such asa minimum and maximum data range values of the historical data for aselected sensor of the wearable medical device or a selectedphysiological measurement.

In one embodiment, historical trending data can be weighted based on theperiod of time between when a measurement was taken to get the data andthe present time. In one embodiment, the earlier or older a data pointor data set is from the present time, the less weight or effect is hason the historical trending data. In another embodiment, the newer orfresher a data point or data set is to the present time, the higherweight or effect is has on the historical trending data. For example, asa wearable medical device takes physiological measurements for aselected period of time the wearable medical device stores the differentphysiological measurements. When the wearable medical device analyzesthe physiological measurement data, the wearable medical device canplace a higher weight value for current measurements and measurementdata that was recently taken, and less weight on measurement data thatwas taken less recently. One advantage or weight the measurement dataaccording to how recently the measurement data was taken can be toaccommodate for a change in data measurements over time. For example asan individual use the wearable medical device for a period of time, theenvironment that the individual is in when the measurements are takencan change over time and/or physiological conditions of the individualcan change over time. More recent data can be weight to accommodate forthe change in environment and/or for physiological changes of theindividual. In one embodiment, the historical trending data can beaveraged or compared to other historical trending data or currentmeasurement data and each data set can be given weighting values. Inanother embodiment, current measurement data can be adjusted or updatedbase on historical trending data, wherein weighting values can beassigned to different data sets or data points in the historicaltrending data.

For example, the data range values of the historical data over aselected period of time for the oxygen level of the blood of a user canbe a minimum of 90% oxygenation and a maximum of 100% oxygenation. Whencurrent measurement data from one or more sensors of the wearablemedical device and/or a separate device is within the data range valuesof the historical data, the wearable medical device can be optimallycalibrated. When current measurement data from one or more sensors ofthe wearable medical device and/or a separate device is not within thedata range values of the historical data, the wearable medical devicemay need to be recalibrated.

When the wearable device is moved or removed from a location on the userand replaced, the wearable medical device can take a measurement usingthe sensor array and compare the measurement against stored previousmeasurements or historical data to determine when to calibrate thewearable medical device and/or what calibrations can be made to one ormore sensors of the wearable medical device or measurement data from oneor more of the sensors of the wearable medical device. For example, whencurrent measurement data is substantially similar to previousmeasurements or historical data the wearable device may be located insubstantially the same location as previously located. In anotherexample, when current measurement data is not substantially similar toprevious measurements or historical data the wearable device may belocated in a different location than the previous location of thewearable device.

FIG. 12 illustrates one exemplary embodiment of a wearable medicaldevice 1210. The wearable medical device 1210 can be a substantiallycircular band with an outer surface 1220 and an inner surface 1230. Inone embodiment, the outer surface 1220 and an inner surface 1230 can bemade of flexible or non-rigid material, such as rubber, polyurethane,and so forth. In another embodiment, the outer circumference 1220 and aninner circumference 1230 can be made of semi-rigid or rigid material,such as plastic, metal, and so forth. In one embodiment, a cavity 1240can be between the outer surface 1220 and an inner surface 1230. Thecavity 1240 can include modules, units, systems, subsystems, or devicesof the wearable medical device 1210. For example, a power source 1250, atouch controller 1260, a communication unit 1270, a controller 1280, amedical measurement sensor or physiological sensor 1282, and/or otherunits located in the cavity 1240 of wearable medical device 1210. In oneembodiment, the communication unit 1270 can wirelessly communicate withan external computing device 1290. In another embodiment, the powersource 1250 can provide power to other units or modules of the wearablemedical device 1210.

In another embodiment, the power source 1250 can be a battery, such as arechargeable battery. The power source 1250 can receive power fromanother power source such as via a cord plugged into a power source orusing wireless power such as inductive wireless charging or resonantwireless charging. In one embodiment, the touch controller 1260 canreceive user input from a user of the wearable medical device 1210. Inone embodiment, a power source 1250, a touch controller 1260, acommunication unit 1270, a controller 1280, a medical measurement sensoror physiological measurement sensor 1282 can be in direct or indirectcommunication with each other. For example, the touch controller 1260receive user input information and communicate the user inputinformation to the controller 1280 and the controller 1280 can have acomputer processor to analyze or process the user input information. Inanother example, the physiological measurement sensor 1282 can take aphysiological measurement and communicate physiological measurement tothe external computing device 1290 via the communication unit 1270.

FIG. 13 illustrates that a wearable medical device 1310 can have aplurality of medical measurement sensor or physiological measurementsensor 1382 and 1384. In one embodiment, the plurality of medicalmeasurement sensor or physiological measurement sensor 1382 and 1384 canbe different types of sensors. The wearable medical device 1310 issubstantially similar to the wearable medical device discussed in thepreceding paragraphs for FIG. 12.

FIG. 14 illustrates a wearable medical device 1410 in communication withseparate device 1420. In one embodiment, the separate device 1420 canalso be a wearable device. In one example, the separate device 1420 canbe a heart rate monitor worn around a chest of an individual. In oneembodiment, the wearable medical device 1410 and the separate device1420 can each take medical measurements and communicate the medicalmeasurement from the wearable medical device 1410 and the separatedevice 1420 or vise versa. In one example, the separate device 1420 canmonitor a heart rate of an individual and communicate the heart rateinformation to the wearable medical device 1410. In another embodiment,the wearable medical device 1410 can be in communication with aplurality of devices, such as separate devices 1420 and 1430. In oneembodiment, separate device 1430 can be a movement sensor, such as anaccelerometer that is attached to or built into a shoe.

FIG. 15 illustrates that a wearable medical device can perform futureforecasting of selected physiological data 1530 based on a selectedhistorical trending of measurement data 1510 and/or current trending ofmeasurement data 1520 taken using one or more sensors of the wearablemedical device and/or a separate device. In one embodiment, selectedhistorical trending of measurement data 1510 is a previous measurementdata set for a selected period of time, for a selected activity, or aselected environment, or for other criteria. FIG. 15 illustrates onexemplary embodiment of the wearable medical device forecasting a spikein a heart rate of an individual based on the historical trending ofmeasurement data 1510 and/or current trending of measurement data 1520.For example, the historical trending of measurement data 1510 shows asubstantially constant heart rate trend and the current trending ofmeasurement data 1520 shows a dip in the heart rate of the individual.In this example, the wearable medical device can compare the a selectedhistorical trending of measurement data 1510 and/or current trending ofmeasurement data 1520 with other data sets or data groups stored on thewearable medical device or the separate device and determine that futuretrending of measurement data 1540 may follow a similar trend to thestored data sets or data groups.

In another embodiment, the wearable medical device can project orforecast selected future medical or physiological measurements of theuser by analyzing collected current measurement data and/or historicalmeasurement data from the wearable medical device and/or the separatedevice. In another embodiment, the wearable medical device can projectselected future medical or physiological events or conditions of theuser by analyzing collected present and/or historical measurement datafrom the wearable medical device and/or the separate device. Forexample, the wearable medical device can collect and store hydrationlevel measurements taken by a sensor of the wearable medical device. Thewearable medical device can analyze the stored hydration levelmeasurements and the current hydration level measurements and based onthe analyzed stored hydration level measurements and the currenthydration level measurements, the wearable medical device can forecastthe probable hydration level of the user in the future.

Selected physiological responses of the user in data measured by one ormore sensors of the wearable medical device can be filtered out and/orcalibrated. Selected physiological responses in data can include:resting heart rate, sweating, increased glucose levels after eating,changes in skin temperature as the ambient temperature changes, andother physiological responses. Selected physiological responses cancause a false positive in sensor measurements or sensor data. Thewearable medical device can filter out undesirable or detrimentalphysiological responses in the data to enable the wearable medicaldevice to efficiently or accurately monitor or measure otherphysiological responses and/or take other medical measurements.

In one embodiment, the wearable medical device can measure a hydrationlevel or change in the hydration level of a user. In one embodiment, thewearable medical device can filter out the effect of rehydration of auser, such as when the user takes a drink or otherwise intakes fluidswhile wearing the wearable medical device. In another example, when auser consumes or intakes more fluids and fuel than the body of the usercan absorb, the wearable medical device can be calibrated to determinethe peak or maximum rehydration level or rate of the body of the usercan absorb fluids or fuel. In another embodiment, the wearable medicaldevice can be calibrated to measure a hydration level of a user of thewearable medical device based on the fluid absorption rate of the user.

In one embodiment, the wearable medical device can use a predefinedaverage or maximum rate of a physiological response of the body tocalibrate one or more sensors of the wearable medical device. In oneembodiment, the wearable medical device can use a predefined average ormaximum rate of a physiological response of the body to filter out dataor measurements taken by one or more sensors of the wearable medicaldevice. For example, the wearable medical device can determine anaverage or maximum rate the body of the user can absorb fluid and filterout and data points in measurement data that exceeds the average ormaximum rate the body of the user can absorb fluid. In one embodiment,the predefined average or maximum rate of a physiological response ofthe body can be based on the body of the individual wearing the wearablemedical device. In another embodiment, the predefined average or maximumrate of a physiological response of the body can be based on the body ofa group of individuals or another individual.

In one embodiment, the wearable medical device can determine the averageor maximum rate the body of the user can absorb by analyzing thehistorical medical measurement data or predetermined medical measurementdata. In another embodiment, the wearable medical device can use theaverage or maximum rate of plurality of individuals by analyzing thehistorical medical measurement data or predetermined medical measurementdata for the plurality of individuals. For example, on average a healthykidney of an individual at rest can excrete 800 to 1,000 milliliters offluid per hour and a person can absorb fluid at a rate of 800 to 1,000milliliters per hour without experiencing a net gain in fluid retainedor absorbed by the body of individual. In another example, when theindividual is running a marathon, the stress of the marathon canincrease vasopressin levels, reducing the excretion capacity of thekidneys of the individual, such as low to excreting 100 milliliters perhour. When the individual running the marathon drinks 800 to 1,000milliliters of fluid per hour, there can be a net gain in fluid retainedor absorbed by the body of the individual.

The wearable medical device can monitor the intake and excretion offluids for an individual or plurality of individuals, store themeasurement information, and analyze the historical medical measurementdata to determine a maximum rate the user can absorb fluid. In anotherembodiment, the wearable medical device can store predetermined medicalmeasurement data, such as public health statistic, and compare and fitor match the predetermined medical measurement data with the medicalmeasurements of the wearable medical device to calibrate data from oneor more sensors of the wearable medical device based on thepredetermined medical measurement data.

In one embodiment, the wearable medical device can use the average sweatrate of an individual or the average sweat rate for a group ofindividuals with the same or similar physical characteristic tocalibrate measurement data from one or more sensors of the wearablemedical device and/or a separate medical device. For example, theaverage person sweats between 0.8 to 1.4 liters per hour duringexercise. In one embodiment, the wearable medical device can account forthe fluid loss from the sweating of the individual by using anindependent medical device to measure the fluid loss of the individualdue to sweating. For example, an individual can measure his naked weighusing a bathroom scale at a selected time of day, such as an hour beforeexercising. After the individual has completed an hour of exercise, theindividual can again measure the naked weigh himself using the bathroomscale. In one embodiment, to attain a more accurate sweat ratemeasurement for the individual, the individual may not use the toilet orconsume any fluids during the period between the first weighing and thesecond weighing. In one embodiment, for each kilogram of weight lostbetween the first weighing and the second weighing, the individual mayhave lost one liter of fluid. In another embodiment, the individual candrink fluids or excrete fluids, such as by using the rest room, betweenthe first weighing and the second weighing and the estimated weights ofthe fluid drunk or excreted can be used to adjust the calibration and/ormedical measurements.

FIG. 16a shows that a wearable medical device can receive selectedcalibration information. In one embodiment, the selected calibrationinformation 1610 can include: user settings 1620, such as calibrationsensitivity 1640; user habit information 1630, such as habitual periodsof time when the user may be inactive or less active (such as whilesleeping or at work). In another embodiment, the selected calibrationinformation 1610 can include: selected time intervals for recalibration,such as how often the wearable medical device recalibrates; and othercalibration information.

FIG. 16b illustrates that the wearable medical device 1610 can receivedata or information, such as user input, from an external device. FIG.16 b illustrates that the wearable medical device can receive data orinformation wiredly, such as via a cable 1620, that can connect andtransfer information to and/or from the wearable medical device 1610. Inanother embodiment, the wearable medical device 1610 can receive data orinformation wirelessly, such as via a Bluetooth connection, Zigbeeconnection. Wi-Fi network, or cellular network.

FIG. 17 illustrates selected locations where a wearable medical devicecan be attached to the body of an individual. In one embodiment, theselected locations on the individual can include: a hand 1710, a wrist1712, an arm 1714, a shoulder 1716, a head 1718, a chest 1720, a stomach1722, a back 1724, a hip 1726, a knee 1728, a ankle or leg 1730, a foot1732, or any other location on the individual. In one embodiment, aplurality of wearable medical devices can be located at differentselected locations on the individual. In one embodiment, the pluralityof wearable medical devices can communicate data or information betweenthe wearable medical devices.

In another embodiment, the wearable medical device can be calibrated toaccount for the affect of environmental factors, such as for an ambienttemperature and/or the environmental humidity, in measurement data. Forexample, when the ambient temperature approximate an individualincreases, the sweat rate of the individual may increase. The wearablemedical device can filter out the measurement data and/or calibrate thesensors of the wearable medical device to account for a sudden decreaseof fluids due to an increase in the ambient temperature rather than aCHF event.

Another technique used to calibrate the wearable medical device can beto use an algorithm to determine when to calibrate the wearable medicaldevice or to reset the baseline for measurement data of the wearablemedical device. For example, when measurement data from one or moresensors of the wearable medical device is atypical or abnormal, thewearable medical device can analyze the measurement data to determine aprobable cause of the abnormality in the measurement data and use aninternal sensor to calibrate one or more sensors of the wearable medicaldevice. In another example, when measurement data from one or moresensors of the wearable medical device is atypical or abnormal, thewearable medical device can analyze the measurement data to determine aprobable cause of the abnormality in the measurement data and use aseparate device to calibrate one or more sensors of the wearable medicaldevice or measurement data of the wearable medical device. In anotherembodiment, the algorithm can be used to recalibrate the measurementdata and/or sensors of the wearable medical device. In anotherembodiment, a verification standard can be used to calibrate thewearable medical device. In one embodiment the verification standard canbe a standard set by a medical standards board, such as the AmericanBoard of Medicine. In another embodiment, the standard separate devicecan be selected to take measurements to compare with the wearablemedical device. For example, the standard separate device can be astandard pulse oximeter device that the American Board of Medicine hasselected as the standard device to use to determine a selectedphysiological measurement.

The wearable medical device can be calibrated using a data orinformation set collected from a selected group of individuals(crowdsourced data). In one embodiment, the crowdsourced data can becategorized and/or filtered to determine similarities in the data forthe selected group of individuals as an aggregate. For example, thecrowdsourced data can be filtered to determine the range of measurementsfor the group of individuals for a medical measurement such as hydrationusing bioimpedance.

The crowdsourced data can be categorized and/or filtered to determinesimilar characteristics in the data for each individual in the group. Inone embodiment, the data for each individual can be analyzed todetermine trends in the measurement data of each individual, such aswhen the individual is at a rest or exercising. The crowdsourced datacan be categorized based on selected criteria such as age, gender,weight, ethnicity, race, fitness level, geographical information, orother demographic criteria. In one embodiment, the wearable medicaldevice can be calibrated based on the crowdsourced data by determiningthe average baseline measurement for the aggregated crowdsourced dataand setting the baseline of the wearable medical device at the averagebaseline calibration for the aggregated crowdsourced data.

In one embodiment, a selected characteristic of the user of the wearablemedical device can be mapped to individuals in the selected group withsimilar characteristics. The baseline calibration of the individuals inthe selected group can be used as the baseline calibration for the userof the wearable medical device. For example, the user can enter hisdemographic information into the wearable medical device and thewearable medical device can map the user's entered demographicinformation to individuals with similar demographic information todetermine expected ranges for selected medical measurements orphysiological measurements taken by the wearable medical device. In oneembodiment, when the medical or physiological measurements or data areoutside a selected range, the wearable medical device can indicate tothe user to calibrate the wearable medical device. In anotherembodiment, when the medical or physiological measurements or data areoutside a selected range, the wearable medical device can automaticallycalibrate the wearable medical device.

In one embodiment, the wearable medical device can calibrate thewearable medical device by adjusting one or more sensors of the wearablemedical device, such as adjusting the power level provided to thesensor. In another embodiment, the wearable medical device can calibratethe wearable medical device by filtering out interference or noise fromone or more sensors of the wearable medical device and/or externalinterference or noise. In another embodiment, the wearable medicaldevice can calibrate the wearable medical device by resetting thebaseline from which the measurement data of one or more sensors of thewearable medical device measures. In another embodiment, the wearablemedical device can calibrate the wearable medical device by adjusting orfiltering the physiological measurement data or medical measurement datafrom one or more sensors of the wearable medical device and/or aseparate device. In one embodiment, adjusting or filtering thephysiological measurement data or medical measurement data from one ormore sensors of the wearable medical device and/or a separate device canbe done using weighted values or weighted scalars as discussed in thepreceding paragraphs.

In another embodiment, the wearable medical device can calibrate thewearable medical device based on personal information entered by theuser, such as the physiology of the user. For example, the wearablemedical device can calibrate the wearable medical device and/or analyzethe medical measurements from the wearable medical device differentlybased on if the user is a male or a female. For example, in comparingthe rate at which men and women of similar physical fitness levelsperspire during physical activity, men sweat more than women andtherefore can lose more bodily fluid for the same amount of exertionover the same period of time. In this example, the wearable medicaldevice can be calibrated to adjust for the rate of perspiration of theuser based on their gender.

In another example, an individual that is physically fit typicallybegins sweating at a lower core body temperature than an individual thatis not physically fit. The wearable medical device can be calibratedbased on the physical fitness level of the user to compensate for whenthe user may begin sweating. In another example, females that are notexerting themselves can have a lower sweat rate than males not exertingthemselves and the wearable medical device can be calibrated tocompensate for the sweat rate based on the gender and exertion level ofthe individual.

The wearable medical device can be calibrated based on the user'sphysiology. In one embodiment, the wearable medical device can calibratemeasurement data based on the fitness level of user of the wearablemedical device. In another embodiment, the wearable medical device cancalibrate measurement data based on the exertion level of theindividual. For example, sweat output per sweat gland during an intenseexercise period can be higher for individuals that are relativelyphysically fit than for individuals that are relatively out of shape.The sweat output per sweat gland for women that are relatively out ofshape may take longer to reach a maximum sweat output per sweat glandthan for women that are relatively physically fit. The wearable medicaldevice can calibrate or adjust the measurement data, such as sweatoutput per sweat gland based on the relative fitness level and gender ofthe user.

In another embodiment, the health condition of the user can be used tocalibrate one or more sensors of the wearable medical device. Forexample, when an individual is sick or unhealthy, the fluid level of theindividual can be lower and cause the skin of the individual to tighten.The wearable medical device can calibrate one or more sensors based onthe tightened skin of the individual. In another embodiment, the healthcondition of the user can be used to calibrate measurement data from oneor more sensors of the wearable medical device. For example, if theindividual is sick and vomiting, the fluid level of the individual maychange at a relatively quicker level than a healthy individual that isnot vomiting fluid or otherwise losing fluid and the wearable medicaldevice can adjust the measurement data based on the health level of theindividual.

In one embodiment, the calibration and/or recalibration of the wearablemedical device can be done automatically, e.g. without any input fromthe patient or user. For example, when the wearable medical devicedetermines that the wearable medical device should be recalibrated, thewearable medical device can use the internal sensor of the wearablemedical device to take recalibration measurements and recalibrate thewearable medical device based on the recalibration measurements. Inanother example, when the wearable medical device determines that thewearable medical device should be recalibrated, the wearable medicaldevice can wait to receive recalibration measurements from a separatedevice. When the wearable medical device receives the recalibratemeasurements from the separate device, the wearable medical device canrecalibrate the wearable medical device based on the recalibratemeasurements from the separate device.

In one embodiment, when the wearable medical device determines that thewearable medical device should be recalibrated, the wearable medicaldevice can send out a beacon or signal to a separate device tocommunicate to the independent medical device to take calibrationmeasurements the next time the user of the wearable medical device usesthe independent medical device. In one embodiment, the independentmedical device can receive a beacon or signal from the wearable medicaldevice verifying that the user is the individual using the independentmedical device to enable the separate device to take a measurement andcommunicate the measurement to the wearable medical device.

The wearable medical device can wirelessly communicate with otherdevices using an optical connection such as an infrared connection, orvia a radio frequency connection, such as a wireless fidelity (Wi-Fi)network. Wi-Fi direct, a Bluetooth connection, a cellular communicationssystem such as a third generation partnership project (3GPP) long termevolution (LTE) connection, device to device (D2D) communication, amachine type communication, or via another type of proprietary wirelessconnection. The cellular communications system can comprise one or morecellular network nodes and one or more Institute of Electrical andElectronics Engineers (IEEE) 802.11-2012 configured access points. Inone embodiment, the one or more cellular networks may be 3rd generationpartnership project (3GPP) long term evolution (LTE) Rel. 8, 9, 10, or11 networks and/or IEEE 802.16p. 802.16n, 802.16m-2011, 802.16h-2010,802.16j-2009.802.16-2009 networks.

The wearable medical device can indicate to the user the accuracy levelor calibration level of the wearable medical device, the sensor array ofthe wearable medical device, or a selected sensor of the wearablemedical device. In one embodiment, the wearable medical device candetermine the accuracy level or calibration level of the wearablemedical device and indicate the accuracy level or calibration levelbased for selected ranges of accuracy or calibration. For example, whenthe wearable medical device is within a range of 85% to 100% accuracylevel, the wearable medical device can indicate that the wearablemedical device is fully calibrated. When the accuracy level of thewearable medical device is within a range of 75% to 84% accuracy, thewearable medical device can indicate that the wearable medical device ismarginally calibrated. When the accuracy level of the wearable medicaldevice is below 75% accuracy, the wearable medical device can indicatethat the wearable medical device may need to be recalibrated. Thewearable medical device can indicate the accuracy or calibration levelusing a sensory indication, such as different color lights or sounds fordifferent accuracy levels.

In one embodiment, the wearable medical device can be recalibratedmanually, e.g. the user can manually take a calibration measurement. Forexample, the wearable medical device can indicate to the user that thewearable medical device should be recalibrated. The user can then usethe internal sensor of the wearable medical device or a separate deviceto take a recalibration measurement and input recalibration measurementinformation into the wearable medical device.

In another embodiment, the wearable medical device can be recalibratedon a periodical basis. In one embodiment, the wearable medical devicecan automatically recalibrate on a periodic basis. For example, thewearable medical device can use the internal sensors or the independentmedical device at a selected time, such as Monday of each week, andrecalibrate the wearable medical device based on the calibrationmeasurements. In one embodiment, the wearable medical device can bemanually recalibrated on a periodic basis. For example, the user cantake a measurement using the internal sensors or the independent medicaldevice at a selected time, such as Monday of each week, and input thecalibration measurements into the wearable medical device. The wearablemedical device can use the calibration measurement to recalibrate thewearable medical device. In one embodiment, the wearable medical devicecan use a sensory indicator, such as a light, sound, or vibration toindicate to the user that the periodic recalibration period has arrived.

When the wearable medical device has come out of alignment or there isinterference with the sensors of the wearable medical device, thewearable medical device can signal the user of the wearable medicaldevice or a 3rd party to calibrate the wearable medical device. In oneembodiment, the wearable medical device can indicate to the user tocalibrate the wearable medical device or that an error has occurred intaking medical measurement by using a sensory indication such as asound, vibration, displaying a message and so forth. For example if thewearable medical device determines that there is interference betweenthe sensors and the location the sensors are taking measurements on theuser, the wearable medical device can display an error message on adisplay screen. In another embodiment, the wearable medical device canalert a third party, such as a medical care giver, that the wearablemedical device is not taking accurate medical measurements and/orindicate to the third party to adjustment or calibration the wearablemedical device.

In one embodiment, the wearable medical device can determine if therehas been a movement of the wearable medical device that has caused it tobecome unaligned. In one embodiment, the wearable medical device can usea movement sensor, such as an accelerometer, to determine when thewearable medical device has come out of alignment. For example, when themovement sensor detects a relatively abrupt movement of the wearablemedical device, the wearable medical device can determine that thewearable medical device has moved out of alignment. In one embodiment,when the wearable medical device has moved out of alignment the wearablemedical device can be moved back into place or realigned. In anotherembodiment, the wearable medical device can be recalibrated or set a newbaseline from which medical measurements or physiological measurementsare measured for the new location of the wearable medical device on theuser's body.

In another embodiment, the movement sensor can be used to continuousrecalibrate, filter, or compensate for movement of the wearable medicaldevice and/or of the user. For example if the wearable medical device isused during an active period of the user, such as when the user isjogging or exercising, the movement sensor can determine that there is aconsistent or constant movement of the wearable medical device and cancompensate, adjust, and/or filter the medical measurement data orphysiological measurement data to remove interference caused by movementof the wearable medical device.

The wearable medical device can be adjusted or calibrated to takedifferent medical measurements when a physiological event, such as acardiac arrhythmia or an episode of patient discomfort, has beendetected. For example, during a normal physiological period the wearablemedical device can use a predetermined or defined set of calibrationparameters in taking medical measurement. In this example, when the userof the wearable medical device undergoes a physiological event, such asa heart attack, low blood pressure, high blood pressure, low bloodsugar, low hydration level, and so forth, the wearable medical devicecan then change the calibration of the sensors of the wearable medicaldevice and/or modify the algorithms used to analyze the measurementinformation.

In one embodiment, the wearable medical device can initiate capturingthe physiological data of a user during a physiological event. Inanother embodiment, the wearable medical device can use an eventrecorder to record and/or store information during a physiologicalevent. In another embodiment, when a physiological event is detected bythe wearable medical device, a third party, such as a caregiver, can bealerted of the physiological event.

The wearable medical device can detect repetitive patterns in themedical measurements and can calibrate the wearable medical device tofilter out the repetitive patterns. In one embodiment, the wearablemedical device can alert the user and/or a third party of repetitivepatterns in the medical measurement data. For example, if the user ofthe wearable medical device has a cardiac episode each time the user hasa high blood pressure measurement or has a high hematocrit measurement,the wearable medical device can determine when the cardiac episode islikely to occur and alert or indicate to the user and/or the third partywhen a cardiac episode is likely to happen.

In one embodiment, the wearable medical device can process or analyze,at the wearable medical device, medical measurement data from one ormore sensors of the wearable medical device and/or medical measurementdata from a separate device. In another embodiment, a separate computingdevice can receive measurement data from one or more sensors of thewearable medical device and/or medical measurement data from a separatedevice and process or analyze medical measurement data. The separatecomputing device can send the processed or analyzed medical measurementdata to the wearable medical device or send a calibration value back tothe wearable medical device to enable the wearable medical device tocalibrate one or more sensors of the wearable medical device and/ormeasurement data of the wearable medical device.

FIG. 18 provides a flow chart 1800 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured toreceive physiological measurement data from a separate device, as inblock 1810. The computer circuitry can be further configured to analyzethe physiological measurement data from the separate device at thewearable medical device, as in block 1820. The computer circuitry canalso be configured to set a baseline for the medical measurement datafrom one or more sensors of the wearable medical device usingphysiological measurement data from the separate device, as in block1830. The computer circuitry can also be configured to take a medicalmeasurement using the one or more sensors of the wearable medicaldevice, as in block 1840.

FIG. 19 provides a flow chart 1900 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured totake a medical measurement using one or more sensors of a wearablemedical device, as in block 1910. The computer circuitry can be furtherconfigured to analyze the medical measurement data from the one or moresensors of the wearable medical device, as in block 1920. The computercircuitry can also be configured to receive medical measurement datausing one or more sensors of a separate device, as in block 1930. Thecomputer circuitry can also be configured to set a baseline value forthe medical measurement data by comparing the medical measurement dataof the wearable medical device with the medical measurement data fromthe separate device, as in block 1940.

FIG. 20 provides a flow chart 2000 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured totake a physiological measurement using one or more sensors of a wearablemedical device, as in block 2010. The computer circuitry can be furtherconfigured to calibrate the sensors of the wearable medical device usingthe physiological measurement data from the wearable medical device, asin block 2020. The computer circuitry can also be configured to receivemeasurement data from a separate device at the wearable medical device,as in block 2030. The computer circuitry can also be configured torecalibrate the sensors of the wearable medical device or medicalmeasurement data from the wearable medical device based on themeasurement data from the separate device, as in block 2040.

FIG. 21 provides a flow chart 2100 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured todetermine one or more unique physiological characteristics of theoperator of the wearable medical device using one or more sensors of thewearable medical device, as in block 2110. The computer circuitry can befurther configured to use the one or more sensors of the wearablemedical device to take unique physiological measurements of the user, asin block 2120. The computer circuitry can also be configured tocalibrate the wearable medical device to the unique physiology of theuser based on the unique physiological characteristics, as in block2130.

FIG. 22 provides a flow chart 2200 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured todetermine one or more unique physiological characteristics of theoperator of the wearable medical device using one or more sensors of aseparate device, as in block 2210. The computer circuitry can be furtherconfigured to communicate the unique physiological information of theuser from the separate device to the wearable medical device, as inblock 2220. The computer circuitry can also be configured to calibratethe wearable medical device to the unique physiology of the user basedon the unique physiological characteristics of the operator, as in block2230.

FIG. 23 provides a flow chart 2300 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured todetermine one or more unique physiological characteristics of a firstuser of the wearable medical device using one or more sensors of aseparate device and the wearable medical device, as in block 2310. Thecomputer circuitry can be further configured to determine one or moreunique physiological characteristics of a second user of the wearablemedical device using one or more sensors of a separate device and thewearable medical device, as in block 2320. The computer circuitry canalso be configured to calibrate the wearable medical device to theunique physiology of the first user or the second user based on theunique physiological characteristics of the first user or the seconduser, as in block 2330. The computer circuitry can also be configured tomonitor one or more selected physiological measurements of the firstuser or the second user of the wearable medical device using thewearable medical device or the separate device, as in block 2340.

FIG. 24 provides a flow chart 2400 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured toreceive continuous, discrete, or periodic physiological measurement froma separate device for a selected period of time, as in block 2410. Thecomputer circuitry can be further configured to take a physiologicalmeasurement using one or more sensors of the wearable medical device, asin block 2420. The computer circuitry can also be configured tocorrelate the physiological measurement from the wearable medical devicewith the physiological measurement from the separate device, as in block2430. The computer circuitry can also be configured to check theaccuracy of the physiological measurement of the wearable medical devicebased on the correlated physiological measurement data of the wearablemedical device and the separate device, as in block 2440.

FIG. 25 provides a flow chart 2500 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured toreceive physiological measurement data using a plurality of sensors of aseparate device or a wearable medical device, as in block 2510. Thecomputer circuitry can be further configured to analyze the measurementdata from the plurality of sensors using a multivariant analysis, as inblock 2520. The computer circuitry can also be configured to determinethe accuracy level of the wearable medical device based on themultivariant analysis results, as in block 2530. The computer circuitrycan also be configured to recalibrate the physiological measurement dataor one or more sensors of the wearable medical device or the separatedevice based on when the accuracy level of the wearable medical deviceor the separate device decrease below a threshold value, as in block2540.

FIG. 26 provides a flow chart 2600 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured totake medical measurement data using the separate device or the wearablemedical device on a continuous, periodic, or discrete basis for aselected period of time, as in block 2610. The computer circuitry can befurther configured to analyze the medical measurement data on acontinuous, periodic, or discrete basis for one or more trends in themedical measurement data, as in block 2620. The computer circuitry canalso be configured to alert a user of the wearable medical device whenthe one or more trends of the medical measurement data exceeds aselected range, as in block 2630.

FIG. 27 provides a flow chart 2700 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured totake medical measurement data using the separate device or the wearablemedical device on a continuous, periodic, or discrete basis for aselected period of time, as in block 2710. The computer circuitry can befurther configured to analyze the medical measurement data on acontinuous, periodic, or discrete basis for one or more trends in themedical measurement data, as in block 2720. The computer circuitry canalso be configured to recalibrate the physiological measurement data orone or more sensors of the wearable medical device or the separatedevice when one or more trends in the medical measurement data exceeds aselected range, as in block 2730.

FIG. 28 provides a flow chart 2800 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured totake a physiological measurement using a non-invasive sensor of awearable medical device, as in block 2810. The computer circuitry can befurther configured to analyze physiological measurement data from thenon-invasive sensor of the wearable medical device, as in block 2820.The computer circuitry can also be configured to recalibrate thephysiological measurement data or one or more sensors of the wearablemedical device based on the physiological measurement data from thenon-invasive sensor of the wearable medical device, as in block 2830.

FIG. 29 provides a flow chart 2900 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured totake a selected physiological measurement of the user using a sensor ofa wearable medical device, as in block 2910. The computer circuitry canbe further configured to monitor the selected physiological measurementof the user to determine when a physiological event occurs based on thephysiological measurement, as in block 2920. The computer circuitry canalso be configured to alert the user or a third party when thephysiological event occurs, as in block 2930.

FIG. 30 provides a flow chart 3000 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured tocontinuously or semi-continuously monitor a selected physiologicalparameter of the user using a wearable medical device, as in block 3010.The computer circuitry can be further configured to perform a trendanalysis of physiological measurement data from the wearable medicaldevice, as in block 3020. The computer circuitry can also be configuredto calibrate the wearable medical device based on the trend analysis ofthe physiological measurement data, as in block 3030.

FIG. 31 provides a flow chart 3100 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured totake a selected physiological measurement of the user using a sensor ofa wearable medical device, as in block 3110. The computer circuitry canbe further configured to take a selected physiological measurement ofthe user using a sensor of a separate device, as in block 3120. Thecomputer circuitry can also be configured to aggregate the physiologicalmeasurement data of the wearable medical device and the physiologicalmeasurement data of the separate device, as in block 3130. The computercircuitry can also be configured to determine orthogonal data points ordata sets in the aggregated physiological measurement data, as in block3140. The computer circuitry can also be configured to calibrate thewearable medical device based on the orthogonal data points or datasets, as in block 3150.

FIG. 32 provides a flow chart 3200 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured totake a selected type medical measurement from a user for a selectedmedical condition using a separate medical device, as in block 3210. Thecomputer circuitry can be further configured to take a different type ofmedical measurement from a user for the same selected medical conditionusing a wearable medical device, as in block 3220. The computercircuitry can also be configured to determine orthogonal data points ordata sets in data from the medical measurement using the separate deviceand data from the medical measurement using the wearable medical device,as in block 3230. The computer circuitry can also be configured tocalibrate the wearable medical device based on the orthogonal datapoints or data sets, as in block 3240.

FIG. 33 provides a flow chart 3300 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured totake a selected type medical measurement from a user for a selectedmedical condition using a separate medical device, as in block 3310. Thecomputer circuitry can be further configured to take a different type ofmedical measurement from a user for the same selected medical conditionusing a wearable medical device, as in block 3320. The computercircuitry can also be configured to analyze the data from the medicalmeasurement using the separate device and data from the medicalmeasurement using the wearable medical device, as in block 3330. Thecomputer circuitry can also be configured to diagnose, monitor, orscreen for a medical condition based on the analyzed data, as in block3340.

FIG. 34 provides a flow chart 3400 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured toselect one or more physiological measurements to monitor using awearable medical device, as in block 3410. The computer circuitry can befurther configured to set a threshold value or range for thephysiological measurements of the wearable medical device, as in block3420. The computer circuitry can also be configured to calibrate thewearable medical device when the data from the physiologicalmeasurements exceeds the threshold value or range, as in block 3430.

FIG. 35 provides a flow chart 3500 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured totake a selected baseline medical measurement using a wearable medicaldevice or a separate device, as in block 3510. The computer circuitrycan be further configured to continuous take a selected medicalmeasurement and compare data of the continuous medical measurement withthe baseline medical measurement data, as in block 3520. The computercircuitry can also be configured to determine when the data of thecontinuous medical measurement exceeds a standard deviation of thebaseline medical measurement, as in block 3530. The computer circuitrycan also be configured to calibrate the wearable medical device when thedata of the continuous medical measurement exceeds the standarddeviation of the baseline medical measurement, as in block 3540.

FIG. 36 provides a flow chart 3600 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured totake a medical measurement using a wearable medical device or a separatedevice, as in block 3610. The computer circuitry can be furtherconfigured to determine when a selected data point or data set show asudden shift from previous selected data point or data set, as in block3620. The computer circuitry can also be configured to calibrate thewearable medical device when the selected data point or data set show asudden shift from a previous selected data point or data set, as inblock 3630.

FIG. 37 provides a flow chart 3700 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured totake a medical measurement using one or more selected sensors of asensor array of a wearable medical device, as in block 3710. Thecomputer circuitry can be further configured to analyze the medicalmeasurement data from the one or more selected sensors, as in block3720. The computer circuitry can also be configured to calibrate thewearable medical device based on the medical measurement data of the oneor more selected sensors, as in block 3730.

FIG. 38 provides a flow chart 3800 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured totake a medical measurement using one or more selected sensors of asensor array of a wearable medical device, as in block 3810. Thecomputer circuitry can be further configured to analyze the medicalmeasurement data using a multivariate analysis or regression analysis,as in block 3820. The computer circuitry can also be configured tocalibrate the wearable medical device based on the medical measurementdata of the multivariate analysis or the regression analysis, as inblock 3830.

FIG. 39 provides a flow chart 3900 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured totake a medical measurement using one or more selected sensors of asensor array of a wearable medical device, as in block 3910. Thecomputer circuitry can be further configured to filter the medicalmeasurement data for data points or a data set for a selected medicalcondition, as in block 3920. The computer circuitry can also beconfigured to determine when a selected medical condition occurs basedon the medical measurement data, as in block 3930.

FIG. 40 provides a flow chart 4000 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured totake a medical measurement using one or more selected sensors of asensor array of a wearable medical device, as in block 4010. Thecomputer circuitry can be further configured to determine when a medicalmeasurement data point or data set from the one or more selected sensorsexceeds a defined threshold value for the medical measurement data, asin block 4020. The computer circuitry can also be configured to excludethe medical measurement data point or data set from the measurementdata, as in block 4030.

FIG. 41 provides a flow chart 4100 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured totake a medical measurement using one or more selected sensors of asensor array of a wearable medical device, as in block 4110. Thecomputer circuitry can be further configured to determine when a medicalmeasurement data point or data set from the one or more selected sensorsexceeds a defined threshold value for the medical measurement data, asin block 4120. The computer circuitry can also be configured tocalibrate the medical measurement data of the sensor based on themedical measurement data point or data set, as in block 4130.

FIG. 42 provides a flow chart 4200 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured totake medical measurements for a group of individuals using a wearablemedical device or a separate device, as in block 4210. The computercircuitry can be further configured to average the medical measurementdata from the group of individuals, as in block 4220. The computercircuitry can also be configured to take a medical measurement of anindividual using the wearable medical device, as in block 4230. Thecomputer circuitry can also be configured to compare the averagedmedical measurement data with the medical measurement data of theindividual, as in block 4240. The computer circuitry can also beconfigured to calibrate the wearable medical device for the individualbased on the compared averaged medical data and the medical measurementdata of the individual, as in block 4250.

FIG. 43 provides a flow chart 4300 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured tocontinuously take medical measurements from an individual using one ormore sensors of at wearable medical device, as in block 4310. Thecomputer circuitry can be further configured to analyze the medicalmeasurement data to determine an error rate of the medical measurements,as in block 4320. The computer circuitry can also be configured torecursively calibrate the wearable medical device based on the errorrate of the medical measurements until the medical measurements reach astable state, as in block 4330.

FIG. 44 provides a flow chart 4400 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured totake a medical measurement from an individual using one or more sensorsof a wearable medical device, as in block 4410. The computer circuitrycan be further configured to determine when there is a sudden movementof the wearable medical device, as in block 4420. The computer circuitrycan also be configured to take a new baseline measurement for thewearable medical device when the wearable medical device suddenly moves,as in block 4430. The computer circuitry can also be configured torecalibrate the wearable medical device based on the new baselinemeasurement, as in block 4440.

FIG. 45 provides a flow chart 4500 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured totake a medical measurement from an individual using one or more sensorsof a wearable medical device, as in block 4510. The computer circuitrycan be further configured to determine when there is a sudden movementof the wearable medical device, as in block 4520. The computer circuitrycan also be configured to alert a user of the wearable medical device ora third party when the wearable medical device suddenly moves, as inblock 4530.

FIG. 46 provides a flow chart 4600 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured totake a medical measurement from an individual using one or more sensorsof a wearable medical device, as in block 4610. The computer circuitrycan be further configured to determine when there is a sudden movementof the wearable medical device, as in block 4620. The computer circuitrycan also be configured to determine the new location of the wearablemedical device using one or more sensors of the wearable medical device,as in block 4630. The computer circuitry can also be configured tocalibrate the wearable medical device based on the new location of thewearable medical device, as in block 4640.

FIG. 47 provides a flow chart 4700 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured tocontinuously or semi-continuously take a medical measurement from anindividual using one or more sensors of a wearable medical device, as inblock 4710. The computer circuitry can be further configured todetermine a discontinuity or gap in the continuous or semi-continuousmedical measurement data, as in block 4720. The computer circuitry canalso be configured to set a new baseline to compare the medicalmeasurement data to, as in block 4730. The computer circuitry can alsobe configured to recalibrate the wearable medical device based on thenew baseline value, as in block 4740.

FIG. 48 provides a flow chart 4800 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured totake an environment measurement using one or more sensors of a wearablemedical device or a separate device, as in block 4810. The computercircuitry can be further configured to take a medical measurement usingone or more sensors of the wearable medical device, as in block 4820.The computer circuitry can also be configured to determine an effect ofthe environmental conditions proximate the user of the wearable medicaldevice on the medical measurement data based on the environmentmeasurement, as in block 4830. The computer circuitry can also beconfigured to adjust or correct shifts in the medical measurement databased on the effect of the environmental conditions, as in block 4840.

FIG. 49 provides a flow chart 4900 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured totake a medical measurement using one or more sensors of a wearablemedical device or a separate device, as in block 4910. The computercircuitry can be further configured to record the medical measurementdata of an individual using the wearable medical device or the separatedevice, as in block 4920. The computer circuitry can also be configuredto analyze the recorded medical measurement data to determine whencurrent medical measurement data indicates a medical condition issubstantially likely to occur, as in block 4930. The computer circuitrycan also be configured to alert the individual or a third party of themedical condition that is substantially likely to occur, as in block4940.

FIG. 50 provides a flow chart 5000 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured totake a first physiological measurement using one or more sensors of awearable medical device or a separate device, as in block 5010. Thecomputer circuitry can be further configured to determine when awearable medical device is moved to a different location on a user ofthe wearable medical device or removed from the body of the user using asensor of the wearable medical device, as in block 5020. The computercircuitry can also be configured to determine when the wearable medicaldevice is replaced on substantially the same location on the user, as inblock 5030. The computer circuitry can also be configured to take aphysiological measurement using one or more sensors of the wearablemedical device, as in block 5040. The computer circuitry can also beconfigured to determine a difference between the first physiologicalmeasurement and the second physiological measurement, as in block 5050.The computer circuitry can also be configured to take a physiologicalmeasurement using one or more sensors of the wearable medical device, asin block 5060.

FIG. 51 provides a flow chart 5100 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured toreceive a predefined average or maximum rate of a physiological responseof a user of a wearable medical device, as in block 5110. The computercircuitry can be further configured to calibrate the wearable medicaldevice based on the predefined average or maximum rate of thephysiological response, as in block 5120.

FIG. 52 provides a flow chart 5200 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured todetermine an average or maximum rate of a physiological response of auser of a wearable medical device, as in block 5210. The computercircuitry can be further configured to calibrate the wearable medicaldevice based on the average or maximum rate of the physiologicalresponse, as in block 5220.

FIG. 53 provides a flow chart 5300 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured toreceive medical measurement data of a plurality of individuals, as inblock 5310. The computer circuitry can be further configured todetermine an average or maximum rate of a physiological response of aplurality of individuals, as in block 5320. The computer circuitry canbe further configured to calibrate the wearable medical device based onthe average or maximum rate of the physiological response of theplurality of individuals, as in block 5330.

FIG. 54 provides a flow chart 5400 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured tomonitor an intake and excretion of fluid of an individual using awearable medical device, as in block 5410. The computer circuitry can befurther configured to analyze the intake and excretion of the fluid todetermine a fluid absorption rate of the individual, as in block 5420.The computer circuitry can be further configured to take a hydrationlevel measurement of the individual using one or more sensors of thewearable medical device, as in block 5430. The computer circuitry can befurther configured to calibrate the hydration level measurement based onthe fluid absorption rate of the individual, as in block 5440.

FIG. 55 provides a flow chart 5500 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured tostore public health static data on the wearable medical device or adevice in communication with the wearable medical device, as in block5510. The computer circuitry can be further configured to take a medicalmeasurement of an individual using a sensor of the wearable medicaldevice, as in block 5520. The computer circuitry can be furtherconfigured to compare the public health static data with the medicalmeasurement data and calibrate the wearable medical device base on thecompared data, as in block 5530.

FIG. 56 provides a flow chart 5600 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured totake a medical measurement of an individual using a sensor of thewearable medical device, as in block 5610. The computer circuitry can befurther configured to analyze the medical measurement data to determinean atypical or abnormal data point or data set of the medicalmeasurement data, as in block 5620. The computer circuitry can befurther configured to determine the probable cause of the atypical orabnormal data point or data set, as in block 5630. The computercircuitry can be further configured to calibrate the wearable medicaldevice based on the probable cause of the atypical or abnormal datapoint or data set, as in block 5640.

FIG. 57 provides a flow chart 5700 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured totake a medical measurement of an individual using a sensor of thewearable medical device, as in block 5710. The computer circuitry can befurther configured to analyze the medical measurement data to determinean atypical or abnormal data point or data set of the medicalmeasurement data, as in block 5720. The computer circuitry can befurther configured to determine the probable cause of the atypical orabnormal data point or data set, as in block 5730. The computercircuitry can be further configured to indicate to the individual orthird party the probable cause of the atypical or abnormal data point ordata set, as in block 5740.

FIG. 58 provides a flow chart 5800 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured tostore medical measurement data for one or more individuals on a wearablemedical device or a device in communication with the wearable medicaldevice, as in block 5810. The computer circuitry can be furtherconfigured to categorize or filter the medical measurement data based onselected characteristics, as in block 5820. The computer circuitry canbe further configured to take a medical measurement of a user using oneor more sensors of the wearable medical device, as in block 5830. Thecomputer circuitry can be further configured to map the medicalmeasurement data of the user to medical measurement data of individualswith substantially similar characteristics to determine a baselinemeasurement point for the user, as in block 5840. The computer circuitrycan be further configured to calibrate the wearable medical device basedon the baseline measurement point, as in block 5850.

FIG. 59 provides a flow chart 5900 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured tostore medical measurement data for one or more individuals on a wearablemedical device or a device in communication with the wearable medicaldevice, as in block 5910. The computer circuitry can be furtherconfigured to take an average of the medical measurement data todetermine a baseline measurement point, as in block 5920. The computercircuitry can be further configured to calibrate the wearable medicaldevice based on the baseline measurement point, as in block 5930.

FIG. 60 provides a flow chart 6000 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured totake a physiological measurement of a user of a wearable medical deviceusing one or more sensors of the wearable medical device, as in block6010. The computer circuitry can be further configured to determine whenthe wearable medical device is miscalibrated, as in block 6020. Thecomputer circuitry can be further configured to adjust the power levelof one or more sensors of the wearable medical device, as in block 6030.

FIG. 61 provides a flow chart 6100 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured toreceive user information of an individual at the wearable medicaldevice, as in block 6110. The computer circuitry can be furtherconfigured to compare the user information with predeterminedcalibration data to determine a calibration coefficient, as in block6120. The computer circuitry can be further configured to calibrate thewearable medical device based on the calibration coefficient, as inblock 6130.

FIG. 62 provides a flow chart 6200 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured todetermine a physiological parameter of the user of a wearable medicaldevice, as in block 6210. The computer circuitry can be furtherconfigured to compare the physiological parameter with predeterminedcalibration data to determine a calibration coefficient, as in block6220. The computer circuitry can be further configured to calibrate thewearable medical device based on the calibration coefficient, as inblock 6230.

FIG. 63 provides a flow chart 6300 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured todetermine a health status of the user of a wearable medical device, asin block 6310. The computer circuitry can be further configured todetermine a calibration coefficient based on the health status of theuser, as in block 6320. The computer circuitry can be further configuredto calibrate the wearable medical device based on the calibrationcoefficient, as in block 6330.

FIG. 64 provides a flow chart 6400 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured toreceive a beacon signal from a wearable medical device at a separatedevice, as in block 6410. The computer circuitry can be furtherconfigured to authenticate the identity of the user of the wearablemedical device, as in block 6420. The computer circuitry can be furtherconfigured to take a calibration measurement using the separate device,as in block 6430. The computer circuitry can be further configured tocommunicate the calibration measurement to the wearable medical device,as in block 6440.

FIG. 65 provides a flow chart 6500 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured toreceive a calibration measurement for a separate device at a wearablemedical device, as in block 6510. The computer circuitry can be furtherconfigured to determine a calibration coefficient based on thecalibration measurement, as in block 6520. The computer circuitry can befurther configured to calibrate the wearable medical device based on thecalibration coefficient, as in block 6530.

FIG. 66 provides a flow chart 6600 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured totake a physiological measurement of a user using a sensor of a wearablemedical device, as in block 6610. The computer circuitry can be furtherconfigured to determine the accuracy level or calibration level of thewearable medical device based on the physiological measurement, as inblock 6620. The computer circuitry can be further configured to indicateto the user the accuracy level or calibration level of the wearablemedical device, a sensor of the wearable medical device, or a sensorarray of the wearable medical device, as in block 6630.

FIG. 67 provides a flow chart 6700 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured todetermine when a wearable medical device is miscalibrated, as in block6710. The computer circuitry can be further configured to indicate to auser of the wearable medical device to recalibrate the wearable medicaldevice, as in block 6720. The computer circuitry can be furtherconfigured to receive recalibration information for the user, as inblock 6730.

FIG. 68 provides a flow chart 6800 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured tomeasure the activity level of a user of the wearable medical device, asin block 6810. The computer circuitry can be further configured todetermine when the user is active based on the activity level, as inblock 6820. The computer circuitry can be further configured tocompensate, adjust, or filter a medical measurement of the wearablemedical device to remove interference caused by movement of the wearablemedical device from the user activity, as in block 6830.

FIG. 69 provides a flow chart 6900 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured totake a physiological measurement of an individual using a wearablemedical device, as in block 6910. The computer circuitry can be furtherconfigured to determine when a selected physiological event of theindividual based on the physiological measurement, as in block 6920. Thecomputer circuitry can be further configured to calibrate the wearablemedical device to take different medical measurements when the selectedphysiological event occurs, as in block 6930.

FIG. 70 provides a flow chart 7000 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured totake a physiological measurement of an individual using a wearablemedical device or a separate device, as in block 7010. The computercircuitry can be further configured to determine when a selectedphysiological event of the individual based on the physiologicalmeasurement, as in block 7020. The computer circuitry can be furtherconfigured to start recording measurement data of the wearable medicaldevice when the physiological event begins, as in block 7030.

FIG. 71 provides a flow chart 7100 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured totake a physiological measurement of an individual using a wearablemedical device or a separate device, as in block 7110. The computercircuitry can be further configured to analyze the physiologicalmeasurement data to determine one or more repetitive patterns in thephysiological measurement data, as in block 7120. The computer circuitrycan be further configured to calibrate the wearable medical device tofilter out the repetitive patterns, as in block 7130.

FIG. 72 provides a flow chart 7200 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical deviceoperable calibrate sensors of the wearable medical device or medicalmeasurements of the wearable medical device. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured totake a physiological measurement of an individual using a wearablemedical device or a separate device, as in block 7210. The computercircuitry can be further configured to analyze the physiologicalmeasurement data to determine one or more repetitive patterns in thephysiological measurement data, as in block 7220. The computer circuitrycan be further configured to alert the individual or a third party ofthe one or more repetitive patterns in the physiological measurementdata, as in block 7230.

FIG. 73 provides a flow chart 7300 to illustrate the functionality ofone embodiment of the computer circuitry with a wearable medical devicefor monitoring medical parameters of a user. The functionality may beimplemented as a method or the functionality may be executed asinstructions on a machine, where the instructions are included on atleast one computer readable medium or one non-transitory machinereadable storage medium. The computer circuitry can be configured toreceive, at the wearable medical device, medical measurement data from amedical measurement sensor attached to the wearable medical device or amedical measurement sensor in communication with the wearable medicaldevice, as in block 7310. The computer circuitry can be furtherconfigured to determine a calibration coefficient for calibrating thewearable medical device based on the medical, as in block 7320. Thecomputer circuitry can be further configured to calibrate the wearablemedical device based on the calibration coefficient, as in block 7330.

In one embodiment, the computer circuitry can be further configured todetermine a movement of the wearable medical device and filter outinterference or noise in the medical measurement data caused by themovement of the wearable device. In another embodiment, the computercircuitry can be further configured to receive a baseline value to useas an initially reference point for the medical measurement data fromthe medical measurement sensor, reset the baseline value based on thecalibration coefficient, and compare the medical measurement data to thereset baseline value to determine a medical condition of an individualusing the wearable medical device. In another embodiment, the computercircuitry can be further configured to filter the medical measurementdata from the medical measurement sensor or smooth the medicalmeasurement data from the medical measurement sensor.

In another embodiment, the computer circuitry can be further configuredto determine the calibration coefficient using a multivariate analysisor a regression analysis. In another embodiment, the computer circuitrycan be further configured to receive environmental measurement data froman environmental measurement sensor attached to the wearable medicaldevice or an environmental measurement sensor in communication with thewearable medical device and determine a calibration coefficient forcalibrating the wearable medical device based on the environmentalmeasurement data. In another embodiment, the calibration coefficient isbased on; demographic information of the user; recursive data points inthe medical measurement data; data collected from a plurality ofindividuals; a sudden shift in the medical measurement data; or previousmedical measurement data of the user.

In another embodiment, the computer circuitry can be further configuredto receive medical measurement data from an other medical measurementsensor, wherein the medical measurement sensor and the other medicalmeasurement sensor provide orthogonal medical measurement data to thewearable medical device. In another embodiment, the computer circuitrycan be further configured to aggregate medical measurement data from themedical measurement sensor and the other medical measurement sensor,wherein the medical measurement sensor and the other medical measurementsensor provide orthogonal medical measurement data.

In another embodiment, the computer circuitry can be further configuredto receive measurement data from an other device and determine acalibration coefficient based on the measurement data from the otherdevice. In another embodiment, the computer circuitry can be furtherconfigured to receive measurement data from another device and determinea calibration coefficient based on a correlation between measurementdata from other device and the medical measurement data from the medicalmeasurement sensor of the wearable medical device.

FIG. 74 illustrates a physiological measurement device 7410 for taking aphysiological measurement of a user. The physiological measurementdevice 7410 can include an authentication module 7420 to authenticatewhen a selected user is using the physiological measurement device. Thephysiological measurement device 7410 can also include a physiologicalsensor 7430 for taking a physiological measurement of the selected userat a time approximate to when the selected user is authenticated. Thephysiological measurement device 7410 can also include a communicatemodule 7440 to communicate physiological measurement information fromthe physiological measurement device to a selected wearable device.

In one embodiment, the authentication module 7420 further comprises aproximity sensor to determine when the selected user is within athreshold distance of the physiological measurement device. In anotherembodiment, the authentication module 7420 can include a proximitysensor that communicates a beacon signal to the selected wearablemedical device and a receiver that receives a beacon signal from thewearable medical device when the wearable medical device is within athreshold distance of the physiological measurement device.

FIG. 75 provides a flow chart 7500 for method for calibrating a wearablemedical device. The method can comprise receiving, at the wearablemedical device, physiological measurement data from one or morephysiological measurement sensors attached to the wearable medicaldevice or in communication with the wearable medical device, as in block7510. The method can further comprise determining an adjustmentparameter for the physiological measurement data from the one or morephysiological measurement sensors, as in block 7520. The method can alsocomprise adjusting the physiological measurement data of the wearablemedical device based on the adjustment parameter, as in block 7530.

In one embodiment, the method can further comprise determining anidentity of the user of the wearable medical device and calibrating thewearable medical device based on the identity of the user. In anotherembodiment, the method can further comprise determining a location ofthe wearable medical device or one or more of the physiologicalmeasurement sensors on the body of a user of the wearable medicaldevice, determining when the wearable medical device or one or more ofthe physiological measurement sensors is relocated to an other locationon the body of a user of the wearable medical device, and calibratingthe wearable medical device or one or more of the physiologicalmeasurement sensors based the other location of the wearable medicaldevice or one or more of the physiological measurement sensors on thebody of a user of the wearable medical device.

In another embodiment, the method can further comprise analyzing thephysiological measurement data for recursive data points in aphysiological measurement data set, and calibrating the wearable medicaldevice or one or more of the physiological measurement sensors based therecursive data points in a physiological measurement data set. Inanother embodiment, the method can further comprise receiving, at thewearable medical device, health information of a user of the wearablemedical device, and calibrating the wearable medical device or one ormore of the physiological measurement sensors based on the healthinformation of the user. In another embodiment, the method can furthercomprise determining one or more repetitive data points or repetitivedata sets of the physiological measurement data and calibrating thephysiological measurement data based on the repetitive data points orrepetitive data sets of the physiological measurement data.

In another embodiment, the method can further comprise determining anerror rate of the physiological measurement data and adjusting thewearable medical device or one or more of the physiological measurementsensors when the error rate of the physiological measurement dataexceeds a selected error rate threshold. In another embodiment, themethod can further comprise determining a standard deviation of thereceived physiological measurement data and adjusting the wearablemedical device or one or more of the physiological measurement sensorswhen the standard deviation of the physiological measurement dataexceeds a selected standard deviation threshold. In another embodiment,the method can further comprise determining an accuracy rate of thephysiological measurement data, and adjusting the wearable medicaldevice or one or more of the physiological measurement sensors when theaccuracy rate of the physiological measurement data decreases belowselected accuracy rate threshold.

The invention claimed is:
 1. A system, comprising: a first medicaldevice that is wearable by a user, the first medical device comprising afirst sensor that is configured to take a first measurement of a firstphysiological parameter of the user; a second medical device comprisinga second sensor that is configured to take a second measurement of asecond physiological parameter of the user, wherein: the secondmeasurement is different from the first measurement; the firstmeasurement does not measure the second physiological parameter; and thefirst physiological parameter and the second physiological parameterchange as a third physiological parameter changes; and a processingdevice communicatively coupled to the first medical device and thesecond medical device, the processing device configured to: collectfirst measurement data from the first sensor; collect second measurementdata from the second sensor; determine an orthogonal data point or anorthogonal data set between the first measurement data and the secondmeasurement data, wherein an orthogonal data point or an orthogonal dataset corresponds to: the first measurement data being indicative of thethird physiological parameter; and the second measurement data beingindicative of the third physiological parameter; and determine the thirdphysiological parameter using the first sensor of the first medicaldevice or the second sensor of the second medical device, wherein: thefirst measurement data is correlated with the third physiologicalparameter by the orthogonal data point or the orthogonal data set; andthe orthogonal data point or the orthogonal data set enables the firstmedical device to indirectly measure, by the first sensor, the thirdphysiological parameter by directly measuring the first physiologicalparameter.
 2. The system of claim 1, wherein: the first sensor acquiresthe first measurement data by non-invasive measurement; or the secondsensor acquires the second measurement data by invasive measurement. 3.The system of claim 1, wherein the processing device is configured todetermine the third physiological parameter using the first sensor ofthe first medical device in response to the orthogonal data point or theorthogonal data set having a threshold degree of orthogonality.
 4. Thesystem of claim 1, wherein: the first sensor takes the first measurementcontinuously; and the second sensor takes the second measurementnon-continuously, periodically, or when manually prompted by the user.5. The system of claim 4, wherein the processing device is furtherconfigured to determine an error rate for the first measurement takencontinuously.
 6. The system of claim 5, wherein the processing device isfurther configured to recursively calibrate the first medical devicebased on the error rate.
 7. The system of claim 1, wherein: theorthogonal data point or the orthogonal data set comprises a firstmeasurement of the first measurement data and a second measurement ofthe second measurement data; and the first measurement and the secondmeasurement are taken from the user approximately contemporaneously. 8.The system of claim 1, wherein the processing device is configured todetermine the third physiological parameter using the first sensor ofthe first medical device in response to a current measurement value ofthe third physiological parameter exceeding a standard deviation of abaseline measurement value for the third physiological parameter.
 9. Thesystem of claim 1, wherein the orthogonal data point or the orthogonaldata set is determined using a multivariate analysis or a regressionanalysis.
 10. A device, comprising: a first sensor that is configured totake a first measurement of a first physiological parameter of a user;and a processing device coupled to the first sensor and communicativelycoupled to a medical device, the processing device configured to:collect first measurement data from the first sensor; receive secondmeasurement data from the medical device, wherein: the medical device isconfigured to take a second measurement of a second physiologicalparameter, the second measurement being different from the firstmeasurement; the second measurement does not measure the firstphysiological parameter; and the first physiological parameter and thesecond physiological parameter change as a third physiological parameterchanges; determine an orthogonal data point or an orthogonal data setbetween the first measurement data and the second measurement data,wherein the orthogonal data point or the orthogonal data set correspondsto: the first measurement data being indicative of the thirdphysiological parameter; and the second measurement data beingindicative of the third physiological parameter; and determine the thirdphysiological parameter by correlating the third physiological parameterwith the orthogonal data point or the orthogonal data set.
 11. Thedevice of claim 10, further comprising an environmental sensor, whereinthe processing device is further configured to: receive, from theenvironmental sensor, environmental data on an environmental conditionof an environment of the user, wherein the third physiological parameteris influenced by the environment of the user; and determine the thirdphysiological parameter based on the environmental data and: theorthogonal data point; or the orthogonal data set.
 12. The device ofclaim 10, wherein the processing device is further configured to:receive information regarding a unique physiological characteristic ofthe user, wherein the third physiological parameter is influenced by theunique physiological characteristic of the user; and determine the thirdphysiological parameter based on the unique physiological characteristicand: the orthogonal data point; or the orthogonal data set.
 13. Thedevice of claim 10, wherein the processing device is further configuredto: receive third measurement data that: corresponds to the firstphysiological parameter; and is crowd-sourced from a set of other users;receive fourth measurement data that: corresponds to the secondphysiological parameter; and is crowd-sourced from the set of otherusers; determine an orthogonality between: the third measurement dataand the second measurement data; the fourth measurement data and thefirst measurement data; or the third measurement data and the fourthmeasurement data; and calibrate the first sensor based on theorthogonality.
 14. The device of claim 10, wherein the processing deviceis further configured to forecast a future value for the thirdphysiological parameter based on the orthogonal data point or theorthogonal data set.
 15. A method, comprising: receiving, at aprocessing device, first measurement data from a wearable medicaldevice, wherein: the first measurement data directly corresponds to afirst physiological parameter; and the first measurement data does notcorrespond to a second physiological parameter; receiving, at theprocessing device, second measurement data from a separate medicaldevice that is separate from the wearable medical device, wherein: thesecond measurement data directly corresponds to the second physiologicalparameter; and the second measurement data does not correspond to thefirst physiological parameter, determining, by the processing device,orthogonal data between the first measurement data and the secondmeasurement data; and determining, by the processing device, a thirdphysiological parameter based on the orthogonal data.
 16. The method ofclaim 15, further comprising: determining, by the processing device, anerror rate for the first measurement data; and determining, by theprocessing device and based on the error rate, a recalibration rate forthe wearable medical device.
 17. The method of claim 15, wherein: thefirst measurement data comprises a first measurement; the secondmeasurement data comprises a second measurement; the first measurementand the second measurement are taken from a user of the wearable medicaldevice approximately contemporaneously; and the first measurement andthe second measurement are orthogonal and form at least a part of theorthogonal data.
 18. The method of claim 15, further comprisingreceiving, at the processing device, information corresponding to ahabit of a user of the wearable medical device, wherein determining thethird physiological parameter includes correlating the informationcorresponding to the habit of the user with the orthogonal data.
 19. Themethod of claim 15, further comprising setting, by the processingdevice, a baseline measurement for the third physiological parameterbased on the orthogonal data.
 20. The method of claim 15, furthercomprising: receiving, at the processing device, a first value for thethird physiological parameter from the wearable medical device;receiving, at the processing device, a second value for the thirdphysiological parameter from the separate medical device; determining,by the processing device, whether the first value matches the secondvalue; and in response to the first value not matching the second value,calibrating, by the processing device, the orthogonal data using thefirst value and the second value.